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The Unscented Kalman filter (UKF) based on maximum correntropy criterion (MCC) is robust to heavy-tailed non-Gaussian noise. However, the approximate linear measurement equation obtained by statistical linearization technique may be not accurate enough since it only uses prior information. In this paper, a robust stable iterative maximum correntropy criterion UKF (RS-IMCC-UKF) is proposed by using nonlinear measurement function directly and numerical stability methods. Different from the existing UKF algorithms, we only need to perform one-time unscented transformation in each filtering cycle, reducing the execution time of algorithm. Then a nonlinear enhancement model is constructed to handle predictions and observations simultaneously, which will be included in the cost function of robust MCC. In the process of iterative solution, thanks to the latest iteration values which are used to update the measurement information, RS-IMCC-UKF has more accurate results compared with traditional filters. In order to avoid non-positive definite characteristic in covariance matrix and the inverse operation in ill-conditioned matrix, the hyperbolic QR decomposition and Moore–Penrose pseudo-inversion are introduced to improve the numerical stability of the algorithm. Finally, a target tracking is modeled to verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Concept factorization (CF) has shown the effectiveness in the field of data clustering. In this paper, a novel and robust semi-supervised CF method, called correntropy based semi-supervised concept factorization with adaptive neighbors (CSCF), is proposed with improved performance in clustering applications. Specifically, on the one hand, the CSCF method adopts correntropy as the cost function to increase the robustness for non-Gaussian noise and outliers, and combines two different types of supervised information simultaneously for obtaining a compact low-dimensional representation of the original data. On the other hand, CSCF assigns the adaptive neighbors for each data point to construct a good data similarity matrix for reducing the sensitiveness of data. Moreover, a generalized version of CSCF is derived for enlarging the clustering application ranges. Analysis is also presented for the relationship of CSCF with several typical CF methods. Experimental results have shown that CSCF has better clustering performance than several state-of-the-art CF methods. [ABSTRACT FROM AUTHOR]

Multi-view clustering has received widespread attention owing to its effectiveness by integrating multi-view data appropriately, but traditional algorithms have limited applicability to large-scale real-world data due to their high computational complexity and low robustness. Focusing on the aforementioned issues, we propose an efficient and robust multi-view clustering algorithm with anchor graph regularization (ERMC-AGR). In this work, a novel anchor graph regularization (ARG) is designed to improve the quality of the learned embedded anchor graph (EAG), and the obtained EAG is decomposed by nonnegative matrix factorization (NMF) under correntropy criterion to acquire clustering results directly. Different from the traditional graph regularization that needs to construct a large-scale Laplacian matrix pertaining to the all-sample graph, our lightweight AGR, constructed from the perspective of anchors, can reduce the computational complexity significantly while improving the EAG quality. Moreover, a factor matrix of NMF is constrained to be the cluster indicator matrix to omit additional k-means after optimization. Subsequently, correntropy is utilized to improve the effectiveness and robustness of ERMC-AGR owing to its promising performance to complex noises and outliers. Extensive experiments on real-world datasets and noisy datasets show that ERMC-ARG can improve the clustering efficiency and robustness while ensuring comparable or even better effectiveness. [ABSTRACT FROM AUTHOR]

Proposed is a novel variable step size normalized subband adaptive filter algorithm, which assigns an individual step size for each subband by minimizing the mean square of the noise-free a posterior subband error. Furthermore, a noniterative shrinkage method is used to recover the noise-free priori subband error from the noisy subband error signal. Simulation results using the colored input signals have demonstrated that the proposed algorithm not only has better tracking capability than the existing subband adaptive filter algorithms, but also exhibits lower steady-state error. [ABSTRACT FROM AUTHOR]

Recently, the sign subband adaptive filter (SSAF) algorithm has obtained great attention, due to its robustness against impulsive noises and decorrelating property for correlated input signals. However, the performance of the algorithm in the steady-state is not analyzed. In this paper, we study the steady-state mean-square-deviation (MSD) behavior of the SSAF algorithm by using energy conservation relation, Price׳s theorem and some reasonable assumptions. Simulation results in different system identification scenarios (including the input signals, tap lengths, impulsive noises, number of subbands, and step sizes) are provided to support our theoretical analysis. [ABSTRACT FROM AUTHOR]

In order to reduce the steady-state misalignment of real-coefficient proportionate affine projection sign algorithm (RP-APSA) for sparse impulse responses, a memory RP-APSA is proposed in this paper by exploiting the historical values of proportionate factors, called MP-APSA. Further, to improve the robustness of MP-APSA and the recently proposed memory-improved RP-APSA (MIP-APSA) for impulse responses with mutative sparseness, two sparseness-controlled algorithms (SC-MP-APSA and SC-MIP-APSA) are developed by estimating the sparseness of the impulse response at each iteration. Simulation results in the context of acoustic echo cancellation show that the proposed algorithms retain good robustness against impulsive interferences. More importantly, the proposed sparseness-controlled algorithms can not only obtain low steady-state misalignment in both the sparse and dispersive situations, but also adapt to the variations in the sparseness of the impulse response. [ABSTRACT FROM AUTHOR]

Multiview clustering (MVC) has attracted considerable attention owing to its remarkable capacity to reconcile diverse information from multiple perspectives. However, traditional MVC generally has a narrow scope of application owing to its limited efficiency. Consequently, various efficient MVC (EMVC) methods have emerged recently. Despite their promising performance, these EMVC methods still have several unresolved issues: (1) They suffer from reduced effectiveness caused by representation non-alignment across views and information mismatch between stages, and (2) they fail to efficiently resist complex noises and outliers. To address these issues, we propose an efficient correntropy-based multiview clustering method with alignment discretization (ECMCAD). Specifically, a correntropy-based multipartition learning model was developed to efficiently learn view-specific robust partition-level representations. Additionally, a novel alignment discretization strategy was designed to align the learned cross-view representations into a consensus discrete indicator to integrate representation learning, representation alignment, and discrete label acquisition into a unified framework. Furthermore, an efficient alternating optimization method was developed to solve the model. Numerous experiments illustrated the superiority of the proposed method over state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Concept factorization (CF), unlike nonnegative matrix factorization (NMF), can handle data with negative values by approximating the original data with two low-dimensional nonnegative matrices and itself. Nevertheless, existing CF-based methods continue to suffer from the two issues specified as follows: (1) Their effectiveness is reduced by the high degree of factorization freedom and the two-stage mismatch between factorization and category acquisition, and (2) their robustness drops significantly when dealing with complex noise. In response to the aforementioned issues, we propose a robust spectral-embedded bilateral orthogonal concept factorization (RSOCF) model for clustering. It constrains the factor matrices as orthogonal matrices to decrease the freedom and obtain samples' categories directly after factorization, which can significantly improve clustering effectiveness. Moreover, correntropy is introduced into RSOCF to improve its robustness to complex noise. To optimize the non-convex RSOCF model, a half-quadratic-based algorithm is devised. Numerous experiments demonstrate that RSOCF surpasses other state-of-the-art methods in terms of clustering effectiveness and robustness. • A novel robust concept factorization-based clustering model is proposed. • A fast algorithm is proposed to optimize the non-convex model. • Numerous experiments validate its clustering effectiveness and robustness. [ABSTRACT FROM AUTHOR]

Converging evidence increasingly suggests that psychiatric disorders, such as major depressive disorder (MDD) and autism spectrum disorder (ASD), are not unitary diseases, but rather heterogeneous syndromes that involve diverse, co-occurring symptoms and divergent responses to treatment. This clinical heterogeneity has hindered the progress of precision diagnosis and treatment effectiveness in psychiatric disorders. In this study, we propose BPI-GNN, a new interpretable graph neural network (GNN) framework for analyzing functional magnetic resonance images (fMRI), by leveraging the famed prototype learning. In addition, we introduce a novel generation process of prototype subgraph to discover essential edges of distinct prototypes and employ total correlation (TC) to ensure the independence of distinct prototype subgraph patterns. BPI-GNN can effectively discriminate psychiatric patients and healthy controls (HC), and identify biological meaningful subtypes of psychiatric disorders. We evaluate the performance of BPI-GNN against 11 popular brain network classification methods on three psychiatric datasets and observe that our BPI-GNN always achieves the highest diagnosis accuracy. More importantly, we examine differences in clinical symptom profiles and gene expression profiles among identified subtypes and observe that our identified brain-based subtypes have the clinical relevance. It also discovers the subtype biomarkers that align with current neuro-scientific knowledge. • We propose a new GNN architecture with prototype learning for psychiatric diagnosis. • We successfully identify biological meaningful subtypes of psychiatric disorders. • We observe subtype biomarkers that closely correspond to neuroimaging findings. • Our framework achieves the best performance on three psychiatric datasets. [ABSTRACT FROM AUTHOR]

Owing to its ability to handle negative data and promising clustering performance, concept factorization (CF), an improved version of non-negative matrix factorization, has been incorporated into multi-view clustering recently. Nevertheless, existing CF-based multi-view clustering methods still have the following issues: (1) they directly conduct factorization in the original data space, which means its efficiency is sensitive to the feature dimension; (2) they ignore the high degree of factorization freedom of standard CF, which may lead to non-uniqueness factorization thereby causing reduced effectiveness; (3) traditional robust norms they used are unable to handle complex noises, significantly challenging their robustness. To address these issues, we establish a fast multi-view clustering via correntropy-based orthogonal concept factorization (FMVCCF). Specifically, FMVCCF executes factorization on a learned consensus anchor graph rather than directly decomposing the original data, lessening the dimensionality sensitivity. Then, a lightweight graph regularization term is incorporated to refine the factorization process with a low computational burden. Moreover, an improved multi-view correntropy-based orthogonal CF model is developed, which can enhance the effectiveness and robustness under the orthogonal constraint and correntropy criterion, respectively. Extensive experiments demonstrate that FMVCCF can achieve promising effectiveness and robustness on various real-world datasets with high efficiency. [ABSTRACT FROM AUTHOR]

Least squares (LS) estimation is simple yet effective for parameter estimation. Most real-world problems are nonlinear. In practice, nonlinear LS problems are linearized first and then solved by a linear LS estimator. However, since these methods solve a nonlinear LS problem by a linear way, there is room for improvement only if estimators being nonlinear in the original measurement. In this article, an LS approach is proposed for nonlinear LS problems, along with theoretical analysis and justification. First, for a general nonlinear measurement model with any form of noise, a unified linearization (UL) approach is proposed under certain assumptions and a linearized least squares (LLS) estimator is defined based on the UL model. Second, an augmented nonlinear least squares (ANLS) estimator is proposed based on LLS estimation. In the ANLS estimator, the original measurement is augmented by its nonlinear conversion intended to further utilize the nonlinear measurement. Moreover, we prove that the ANLS estimator outperforms LLS estimation-based estimators. Finally, the UL and the ANLS estimator are applied to source localization with time difference of arrival and/or frequency difference of arrival measurement, and a two-step ANLS localization algorithm is developed. The performance of the proposed algorithm is evaluated via simulation of some typical localization scenarios. [ABSTRACT FROM AUTHOR]

Background noise and scale variation are common problems that have been long recognized in crowd counting. Humans glance at a crowd image and instantly know the approximate number of human and where they are through attention the crowd regions and the congestion degree of crowd regions with a global receptive field. Hence, in this paper, we propose a novel feedback network with Region-Aware block called RANet by modeling human's Top-Down visual perception mechanism. Firstly, we introduce a feedback architecture to generate priority maps that provide prior about candidate crowd regions in input images. The prior enables the RANet pay more attention to crowd regions. Then we design Region-Aware block that could adaptively encode the contextual information into input images through global receptive field. More specifically, we scan the whole input images and its priority maps in the form of column vector to obtain a relevance matrix estimating their similarity. The relevance matrix obtained would be utilized to build global relationships between pixels. Our method outperforms state-of-the-art crowd counting methods on several public datasets. [ABSTRACT FROM AUTHOR]

We introduce an innovative and mathematically rigorous definition for computing common information from multi-view data, drawing inspiration from Gács-Körner common information in information theory. Leveraging this definition, we develop a novel supervised multi-view learning framework to capture both common and unique information. By explicitly minimizing a total correlation term, the extracted common information and the unique information from each view are forced to be independent of each other, which, in turn, theoretically guarantees the effectiveness of our framework. To estimate information-theoretic quantities, our framework employs matrix-based Rényi's α -order entropy functional, which forgoes the need for variational approximation and distributional estimation in high-dimensional space. Theoretical proof is provided that our framework can faithfully discover both common and unique information from multi-view data. Experiments on synthetic and seven benchmark real-world datasets demonstrate the superior performance of our proposed framework over state-of-the-art approaches. • We formulate a mathematically definition of common information for multiview data. • We propose a multiview learning method that discerns common and unique information. • Our method is scalable to data more than two view, supported by theoretical proof. • Experimental results substantiate the efficacy of the proposed framework. [ABSTRACT FROM AUTHOR]

Accurate cluster photovoltaic power prediction (CPPP) is crucial for the operation and control of renewable energy grid-connected power systems. The traditional modeling strategies for CPPP such as direct aggregation (DA) and statistical upscaling (SU) have limitations such as error accumulation and upscaling factor uncertainty. To address these issues, this paper proposed a novel hybrid approach for CPPP by combining machine learning models with an improved SU technique. Firstly, a robust broad learning system (BLS) model, in which the Generalized Maximum Correntropy Criterion (GMCC) is used to replace the original mean square error (MSE) loss in BLS, is proposed to solve the problem of multiple outliers affecting the prediction accuracy of regional cluster stations, and it is called GBLS. Then, the Relevance Vector Machine (RVM) as an effective nonlinear regression model is further utilized to compensate for the prediction errors obtained by the GBLS to form the hybrid prediction model, namely GBLS-RVM. Moreover, to mitigate the uncertainty associated with scaling factors in traditional SU strategy, a new SU strategy is developed to refine the relationship between the reference station and the cluster sub-region, enabling direct modeling for regional power prediction. Finally, data from two PV clusters in different regions of China are used to validate the effectiveness of the proposed model, and the results show that under the improved SU strategy, the GBLS-RVM model, reduced RMSE by approximately 33.7% compared to the traditional BLS model, and the RMSE decreased by 12.89% and 30.2% when compared to traditional DA and traditional SU strategies. • A novel hybrid method for CPPP is developed by combining ML and improved SU technique. • A BLS with GMCC (GMCC-BLS) as a prediction model is developed to handle the outliers in measurement data. • An innovative error correction strategy based on RVM is proposed to improve prediction reliability. • A new SU technique is used to establish direct relationships between reference stations and subregions. [ABSTRACT FROM AUTHOR]

Concept factorization (CF) has attracted widespread attention for its promising performance in document clustering. Among various CF variants, graph-regularized CF is the most impressive type, which can improve clustering effectiveness by exploring structural information. Nevertheless, their clustering efficiency is restricted by the following considerations: (1) the introduction of the full-sample graph is accompanied by an increase in computational complexity; (2) most of them require intensive multiplications in optimization, which impair the optimization efficiency. To address these issues, in this work, we propose an anchor-graph regularized orthogonal concept factorization (AROCF) method to enhance the clustering efficiency and effectiveness in document clustering tasks. Firstly, AROCF approximates the full-sample graph with a small-scale anchor graph to reduce the complexity of graph construction from quadratic to linear. Then, one of the factor matrices is constrained as the cluster indicator matrix in our method, which can avoid extra efficiency loss in K-means after optimization. Finally, an orthogonal constraint is employed to restrict the freedom of factorization to increase the clustering effectiveness. To optimize the AROCF model, we develop a fast optimization strategy by combining the trace and orthogonality of matrices. Extensive experiments on various document datasets demonstrate the effectiveness and efficiency of AROCF. [ABSTRACT FROM AUTHOR]

Graph-based clustering commonly provides promising clustering effectiveness as it can preserve samples' local geometric information. Inspired by it, multi-view graph clustering was developed to integrate complementary information among graphs of diverse views and it has received intensive attention recently. Nevertheless, on the one hand, most existing methods require extra k-means after obtaining embedding representations to generate a discrete cluster indicator, which reduces effectiveness due to the two-stage mismatch. On the other hand, numerous complex noises in real-world multi-view data challenge the robustness of existing clustering methods. In this paper, we established a discrete correntropy-based multi-view anchor-graph clustering (DCMAC) model that not only emphasizes the aforementioned issues but also makes use of anchor graphs to improve the efficiency of the graph construction stage. To optimize this non-convex model, we propose a fast half-quadratic-based coordinate descent strategy to acquire the discrete cluster indicator directly without extra k-means. Furthermore, we extend the DCMAC model to a single-view form and provide optimization strategies for it. Extensive experiments illustrate that the proposed method is effective and robust compared to those advanced baselines. • A new discrete correntropy-based multi-view anchor-graph clustering (DCMAC) model is proposed. • A novel half-quadratic-based coordinate descent algorithm is proposed to solve the DCMAC model. • To handle single-view clustering instances, DCMAC is extended to a single-view form named DCAC. • Numerous experiments demonstrate the outstanding effectiveness and robustness of DCMAC. [ABSTRACT FROM AUTHOR]

Recently, the kernel adaptive filtering algorithm with the generalized maximum correntropy criterion (GMCC) has gained attention due to its excellent nonlinear system modeling ability and robustness to non-Gaussian noise. However, since the default center of the GMCC is located at zero, it hampers its performance in the non-zero mean noise environment. To overcome the problem, this paper proposes a kernel adaptive filtering algorithm based on the GMCC with variable center (GMCC-VC) called generalized kernel maximum correntropy criterion with variable center (GKMCC-VC). In addition, the online vector quantization (VQ) method is introduced to the GKMCC-VC algorithm for suppressing the increasing network size. The probability of divergence, the mean-square steady-state behavior, and the convergence condition of the GKMCC-VC algorithm are also obtained under some assumptions. The potential relationship between the GMCC-VC and the generalized minimum error entropy (GMEE) criteria is discussed in the paper. Finally, the simulation results verify the superiority of the proposed algorithm in modeling nonlinear systems and the correctness of the theoretical model. • This paper proposes the GKMCC-VC algorithm for non-zero mean and non-Gaussian noise. • The steady-state performance of the proposed algorithm is analyzed. • Simulation results show the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Randomized autoencoder (RAE) has attracted much attention due to its strong capability of representation with fast learning speed. However, the mainstream RAEs are still designed for scalar/vector data, which inevitably destroys the structure information of tensor data. To alleviate this deficiency, a novel convolutions based matrix randomized autoencoder (MRAE) is developed for two-dimensional (2D) data in this paper, including a one-side MRAE (OMRAE) exploiting the row or column information and a double-side MRAE (DMRAE) that simultaneously extracts the row and column information by 2 parallel OMRAEs. To reduce meaningless encoded features, the within-class scatter matrix (WSI) and within-class interaction distance (WID) constraints are added into OMRAE resulting WSI-OMRAE and WID-OMRAE, respectively. To demonstrate the superiority, stacked MRAEs are embedded into hierarchical regularized least squares for one-class classification and comparisons with several state-of-the-art methods are provided. The source code would be available at https://github.com/ML-HDU/MRAE. • MRAE can learn from 2D data directly without breaking structure information. • WSI-OMRAE minimizes both reconstruction error and within-class scatter. • WID-OMRAE can learn intrinsic manifold structure with compact representation. • Adopting two-sides OMRAEs can perform row/column feature learning in parallel. [ABSTRACT FROM AUTHOR]

Recurrent broad learning system (RBLS) is an effective learning method for processing sequential data. By replacing the enhancement nodes of the broad learning system with recurrent structure, RBLS obtains the capacity to capture the dynamic characteristics of time series data. However, RBLS is derived under the minimum mean square error (MMSE) criterion, which is sensitive to outliers. Moreover, RBLS is insufficient for online sequential learning. To address these limitations, we propose a novel online robust echo state structure based RBLS (OR-ESBLS). In OR-ESBLS, kernel recursive maximum correntropy (KRMC) is introduced to both enhance the robustness and discover the nonlinear characteristics of feature nodes in an online manner. To reduce the heavy computational requirements caused by the kernel method, a Quasi-Monte Carlo (QMC) based Random Fourier Feature (RFF) is utilized for kernel approximation. Furthermore, we adopt the randomized sparse reservoir as the enhancement nodes of RBLS, which can much more efficiently capture dynamic information of the data in the sequential learning setting. Experiments on both synthetic and real-world datasets are reported. The results show that the proposed OR-ESBLS can provide superior performance in online sequential time series prediction. [ABSTRACT FROM AUTHOR]

• We propose a robot perception intelligence adaptation mechanism and three different data sampling styles of this mechanism. • We theoretically analyze and compare the learning effect of the proposed three different data sampling styles. • We determine the most suitable data sampling style based on the performance availability trade off. • Our work offers guidance for training data collection in the robot perception intelligence adaptation process. Robots need more intelligence to complete perception tasks in uncertain and unstructured environments. This paper presents a new self-evolving home service robot framework that learns new perception skills by using manually labeled data obtained in a new home environment. In this framework, a global model is trained which serves as the starting point of the robots' local model, and an adaptation mechanism is developed in the robot to adapt the initial local model to the new home environment. First, three different data sampling styles are proposed to carry out the adaptation process, and theoretical analysis is given to explain the difference between the proposed three data sampling styles. Second, the most suitable data sampling style for incremental learning for a home service robot is determined. Third, we present a case study of multi-style learning, and the experimental results validate our analysis. The theoretical analysis and experimental results lead us to propose a guideline of data collection and labeling for robots to adapt their perception intelligence in home environments. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) detection plays a vital role in on-line condition monitoring of electrical apparatus in the power systems. However, the noise of PD measurements significantly degrades the performance of detection algorithms. In this paper, we focus on developing an adaptive filtering technique for the PD denoising problem. Heretofore, there are just a few literature reviews addressing the PD denoising based on such a method. The proposed recursive continuous S-shaped (RCSS) algorithm integrates the advantages of recursive strategy and continuous S-shaped function into adaptive noise cancellation (ANC) system, yielding enhanced filtering performance. The proposed algorithm can tackle PD noises in both Gaussian and impulsive scenarios, which is easy to implement in practical applications. The convergence behavior is also analyzed. Extensive simulation and experimental results confirm that the proposed algorithm can address polluted PD pulses, even in excessively noisy conditions, resulting in smaller mean square error (MSE) as compared to other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Surface Electromyography (sEMG) based human–robot interaction has been widely studied, where prediction of human voluntary torques is one of the key issues that have not been well addressed. In this article, a torque prediction method based on collaborative neuromusculoskeletal modeling and adaptive learning, is proposed to overcome the limitation of existing methods. First, an sEMG-torque model is designed in comprehensive consideration of the previous research results, the requirement for subject-specific adjustment and the coupling between the muscle or muscle-tendon length and the adjacent joint angles, where the latter two factors have rarely been considered in the literature. Then, by combining the advantages of the stochastic particle swarm optimization and conjugate gradient algorithms, a collaborative optimization method is designed to calibrate simultaneously the undetermined parameters. Moreover, an adaptive learning method based on Gaussian process regression is proposed to learn and predict the estimation errors in real time, by which it is supposed that the torque prediction accuracy can be improved efficiently. Finally, experiments were carried out to validate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

The extended Kalman filter (EKF) is a method extensively applied in many areas, particularly, in nonlinear target tracking. The optimization criterion commonly used in EKF is the celebrated minimum mean square error (MMSE) criterion, which exhibits excellent performance under Gaussian noise assumption. However, its performance may degrade dramatically when the noises are heavy tailed. To cope with this problem, this paper proposes two new nonlinear filters, namely the linear regression maximum correntropy EKF (LRMCEKF) and nonlinear regression maximum correntropy EKF (NRMCEKF), by applying the maximum correntropy criterion (MCC) rather than the MMSE criterion to EKF. In both filters, a regression model is formulated, and a fixed-point iterative algorithm is utilized to obtain the posterior estimates. The effectiveness and robustness of the proposed algorithms in target tracking are confirmed by an illustrative example. [ABSTRACT FROM AUTHOR]

• A novel correntropy based semi-supervised NMF method is proposed for image clustering. • The proposed method is analysed in terms of convergence, robustness, and computational complexity. • The relationships between the proposed method and several typical NMF based methods are discussed. • Experimental results show the effectiveness and robustness of the proposed method in image clustering tasks. Nonnegative matrix factorization (NMF) is a powerful dimension reduction method, and has received increasing attention in various practical applications. However, most traditional NMF based algorithms are sensitive to noisy data, or fail to fully utilize the limited supervised information. In this paper, a novel robust semi-supervised NMF method, namely correntropy based semi-supervised NMF (CSNMF), is proposed to solve these issues. Specifically, CSNMF adopts a correntropy based loss function instead of the squared Euclidean distance (SED) in constrained NMF to suppress the influence of non-Gaussian noise or outliers contaminated in real world data, and simultaneously uses two types of supervised information, i.e., the pointwise and pairwise constraints, to obtain the discriminative data representation. The proposed method is analyzed in terms of convergence, robustness and computational complexity. The relationships between CSNMF and several previous NMF based methods are also discussed. Extensive experimental results show the effectiveness and robustness of CSNMF in image clustering tasks, compared with several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

An adaptive estimation algorithm based on the Lorentzian norm is proposed for echo cancellation in vehicle hands-free communication systems and video teleconferencing systems, namely the affine-projection Lorentzian (APL) algorithm. By minimizing the Lorentzian norm of the a posteriori error vector with a suitable constraint on the weight vector and providing a dynamic Lorentzian-norm-controlling parameter, the proposed APL algorithm achieves robustness against impulsive disturbances and speeds up convergence for colored input signals. The computational complexity of the APL algorithm is analyzed and a fast recursive filtering method is employed to reduce its complexity. The stability analysis, based on energy-conservation arguments, shows that the APL algorithm converges. Furthermore, its tracking behavior is also investigated and a step size optimizing the tracking performance is derived. Simulation results agree well with the theoretical analysis. Simulation results for channel estimation and in-car echo cancellation scenarios demonstrate that the APL algorithm achieves better performance compared to the maximum-correntropy-criterion, affine-projection-generalized-maximum-correntropy, affine-projection-sign, affine-projection-like M-estimate, and Lorentzian adaptive filtering algorithms in various impulsive interference environments. [ABSTRACT FROM AUTHOR]

Accurate estimation of the state of health (SOH) is crucial for the safe and stable operation of lithium-ion batteries. However, since the labeled values may contain non-Gaussian noise which may lead to a decrease in the effectiveness of traditional data-driven based estimation methods. Therefore, a novel robust stacking integrated learning model (ILM) is proposed to enable accurate SOH estimation under non-Gaussian noises (or outliers) conditions. The mixture correntropy loss (MCL) is used in original extreme learning machine (ELM) and the gated recurrent unit (GRU) frameworks to develop novel robust learning models, i.e MCL-ELM and MCL-GRU, and they are used as the sub-models of the proposed ILM, which takes into account the generalization performance of the model and the overall trend of SOH over the time series. In addition, the entropy weighting method that can well reflect the differences between the sub-models is utilized to reduce the complexity of the stacked ILM. Furthermore, the local tangent space alignment is used to capture the local relationships between different loops to enhance the effectiveness of the extracted health features. Several numerical experiments are performed under different cases to validate the estimation effect of the proposed model by using two publicly available datasets, and the experimental results demonstrate that the maximum RMSE, MAE, and MAPE values of the proposed model are 1.391%, 0.932%, and 1.480%, respectively, under non-Gaussian noises conditions. • Robust ELM and GRU models using MCL are developed. • Novel robust stacking ILM via the combination of the MCL-ELM and MCL-GRU is proposed for SOH estimation. • The EWM is used to obtain the output results of multiple base learners in the proposed ILM. [ABSTRACT FROM AUTHOR]

The brain aggregates meaningless local sensory elements to form meaningful global patterns in a process called perceptual grouping. Current brain imaging studies have found that neural activities in V1 are modulated during visual grouping. However, how grouping is represented in each of the early visual areas, and how attention alters these representations, is still unknown. We adopted MVPA to decode the specific content of perceptual grouping by comparing neural activity patterns between gratings and dot lattice stimuli which can be grouped with proximity law. Furthermore, we quantified the grouping effect by defining the strength of grouping, and assessed the effect of attention on grouping. We found that activity patterns to proximity grouped stimuli in early visual areas resemble these to grating stimuli with the same orientations. This similarity exists even when there is no attention focused on the stimuli. The results also showed a progressive increase of representational strength of grouping from V1 to V3, and attention modulation to grouping is only significant in V3 among all the visual areas. Most of the previous work on perceptual grouping has focused on how activity amplitudes are modulated by grouping. Using MVPA, the present work successfully decoded the contents of neural activity patterns corresponding to proximity grouping stimuli, thus shed light on the availability of content-decoding approach in the research on perceptual grouping. Our work found that the content of the neural activity patterns during perceptual grouping can be decoded in the early visual areas under both attended and unattended task, and provide novel evidence that there is a cascade processing for proximity grouping through V1 to V3. The strength of grouping was larger in V3 than in any other visual areas, and the attention modulation to the strength of grouping was only significant in V3 among all the visual areas, implying that V3 plays an important role in proximity grouping. [ABSTRACT FROM AUTHOR]

The traditional image quality assessments, such as the mean squared error (MSE), the signal-to-noise ratio (SNR), and the Peak signal-to-noise ratio (PSNR), are all based on the absolute error of images. Structural similarity (SSIM) index is another important image quality assessment which has been shown to be more effective in the human vision system (HVS). Although there are many essential differences between MSE and SSIM, some important associations exist between them. In this paper, the associations between MSE and SSIM as cost functions in linear decomposition are investigated. Based on the associations, a bit-allocation algorithm for sparse coding is proposed by considering both the reconstructed image quality and the reconstructed image contrast. In the proposed algorithm, the space occupied by a linear coefficient of a basis in sparse coding is reduced to only 9 to 10 bits, in which 1 bit is used to save the sign of linear coefficient, 3 bits are used to save the number of powers of 10 in scientific notation, and only 5 to 6 bits are used to save the significance digits. The experimental results show that the proposed bit-allocation algorithm for sparse coding can maintain both the image quality and the image contrast well. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF) has been a vital data representation technique, and has demonstrated significant potential in the field of machine learning and data mining. Nonnegative matrix tri-factorization (NMTF) is an extension of NMF, and provides more degrees of freedom than NMF. In this paper, we propose the correntropy based orthogonal nonnegative matrix tri-factorization (CNMTF) algorithm, which is robust to noisy data contaminated by non-Gaussian noise and outliers. Different from previous NMF algorithms, CNMTF firstly applies correntropy to NMTF to measure the similarity, and preserves double orthogonality conditions and dual graph regularization. We adopt the half-quadratic technique to solve the optimization problem of CNMTF, and derive the multiplicative update rules. The complexity issue of CNMTF is also presented. Furthermore, the robustness of the proposed algorithm is analyzed, and the relationships between CNMTF and several previous NMF based methods are discussed. Experimental results demonstrate that the proposed CNMTF method has better performance on real world image and text datasets for clustering tasks, compared with several state-of-the-art methods. • The correntropy based orthogonal nonnegative matrix tri-factorization (CNMTF) algorithm is proposed. • The robustness of CNMTF is analyzed by make a comparison with several robust NMF methods. • The relationships between CNMTF and several previous NMF methods are also discussed. [ABSTRACT FROM AUTHOR]

• A variant of the quantized minimum error entropy (QMEE) algorithm is proposed. • The mean square convergence performance analysis for QMEE algorithm is given. • The theoretical values of the steady-state EMSE for QMEE algorithm are validated by simulations. • QMEE algorithm is applied in electroencephalo (EEG) de-noising task. Adaptive filtering algorithms have been widely used in many areas, among which the minimum error entropy (MEE) algorithm is a superior choice, due to its excellent performance in the non-Gaussian noise situations. However, the computational complexity of the MEE algorithm is expensive, which leads to the computational bottlenecks, especially for large-scale datasets. In order to address the problem, we propose an adaptive filtering algorithm based on the quantized minimum error entropy (QMEE) criterion with an online quantization method, named QMEE algorithm. Moreover, we analyze the transient behavior characteristic and derive an approximate analytical expression for the steady-state excess mean square error (EMSE) based on the Taylor expansion. The extensive simulation results in linear modeling and electroencephalogram (EEG) denoising task demonstrate that the proposed method can outperform other robust adaptive filtering algorithms. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF) has attracted increasing attention in data mining and machine learning. However, existing NMF methods have some limitations. For example, some NMF methods seriously suffer from noisy data contaminated by outliers, or fail to preserve the geometric information of the data and guarantee the sparse parts-based representation. To overcome these issues, in this paper, a robust and sparse NMF method, called correntropy based dual graph regularized nonnegative matrix factorization with local coordinate constraint (LCDNMF) is proposed. Specifically, LCDNMF incorporates the geometrical information of both the data manifold and the feature manifold, and the local coordinate constraint into the correntropy based objective function. The half-quadratic optimization technique is utilized to solve the nonconvex optimization problem of LCDNMF, and the multiplicative update rules are obtained. Furthermore, some properties of LCDNMF including the convergence, relation with gradient descent method, robustness, and computational complexity are analyzed. Experiments of clustering demonstrate the effectiveness and robustness of the proposed LCDNMF method in comparison to several state-of-the-art methods on six real world image datasets. • The LCDNMF algorithm is proposed. • The convergence and relation with gradient descent method of LCDNMF is analyzed. • The robustness and computational complexity of LCDNMF is also analyzed. • Experiment results show the effectiveness and robustness of LCDNMF. [ABSTRACT FROM AUTHOR]

• Correntropy is introduced to the registration problem as a distance metric. • A closed-form solution is given out at each iterative step of the algorithm. • Pointwise correspondences are used to make sure the efficiency. • It's a general framework for m-D point set registration. The iterative closest point (ICP) algorithm is fast and accurate for rigid point set registration, but it works badly when handling noisy data or point clouds with outliers. This paper instead proposes a novel method based on the ICP algorithm to deal with this problem. Firstly, correntropy is introduced into the rigid registration problem which could handle noises and outliers well, and then a new energy function based on maximum correntropy criterion is proposed. After that, a new ICP algorithm based on correntropy is proposed, which performs well in dealing with rigid registration with noises and outliers. This new algorithm converges monotonically from any given parameters, which is similar to the ICP algorithm. Experimental results demonstrate its accuracy and robustness compared with the traditional ICP algorithm. [ABSTRACT FROM AUTHOR]

Along with the explosive growing of data, semi-supervised learning attracts increasing attention in the past years due to its powerful capability in labeling unlabeled data and knowledge mining. As an emerging method, the semi-supervised extreme learning machine (SSELM), that builds on ELM, has been developed for data classification and shown superiorities in learning efficiency and accuracy. However, the optimization of SSELM as well as most of the other ELMs is generally based on the mean square error (MSE) criterion, which has been shown less effective in dealing with non-Gaussian noises. In this paper, a robust regularized correntropy criterion based SSELM (RC-SSELM) is developed. The optimization of the output weight matrix of RC-SSELM is derived by the fixed-point iteration based approach. A convergent analysis of the proposed RC-SSELM is presented based on the half-quadratic optimization technique. Experimental results on 4 synthetic datasets and 13 benchmark UCI datasets are provided to show the superiorities of the proposed RC-SSELM over SSELM and other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Robust matrix completion aims to recover a low-rank matrix from a subset of noisy entries perturbed by complex noises. Traditional matrix completion algorithms are always based on $l_2$ -norm minimization and are sensitive to non-Gaussian noise with outliers. In this paper, we propose a novel robust and fast matrix completion method based on the maximum correntropy criterion (MCC). The correntropy-based error measure is utilized instead of the $l_2$ -based error norm to improve robustness against noise. By using the half-quadratic optimization technique, the correntropy-based optimization can be transformed into a weighted matrix factorization problem. Two efficient algorithms are then derived: an alternating minimization-based algorithm and an alternating gradient descent-based algorithm. These algorithms do not require the singular value decomposition (SVD) to be calculated for each iteration. Furthermore, an adaptive kernel width selection strategy is proposed to accelerate the convergence speed as well as improve the performance. A comparison with existing robust matrix completion algorithms is provided by simulations and shows that the new methods can achieve better performance than the existing state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Robust sparse signal recovery against impulsive noise is a core issue in many applications. Numerous methods have been proposed to recover the sparse signal from measurements corrupted by various impulsive noises, but most of them either lack theoretical guarantee for robust sparse recovery or are not efficient enough for large-scale problems. To this end, a general optimization problem for robust sparse signal recovery, which includes many existing works as concrete instances, is analyzed by a freshly defined Double Null Space Property (DNSP), and its solution is proved to be able to robustly reconstruct the sparse signal under mild conditions. Moreover, for computational tractability, weakly convex sparsity-inducing penalties are applied to the general problem, and properties of the solution to the resultant non-convex problem are further studied. Based on these properties, an algorithm named Robust Projected Generalized Gradient (RPGG) is devised to solve the weakly convex problem. Theoretical results prove that the sparse signal can be precisely reconstructed by RPGG from compressive measurements with sparse noise or robustly recovered from those with impulsive noise. Meanwhile, simulations demonstrate that RPGG with tuned parameters outperforms other robust sparse recovery algorithms. [ABSTRACT FROM AUTHOR]

A bias‐compensated normalised least mean square (BCNLMS) algorithm with variable step size (denoted as BCVNLMS) is developed in this work to improve the accuracy and convergence speed for the three‐phase power system (TPPS) frequency estimation in circumstance of the noise and harmonic interference. Taking into account the problem of bias estimation caused by the mixed noises in the acquisition signal of the TPPS, the NLMS algorithm with an unbiased criterion (called BCNLMS) was employed to compensate the bias and improve the estimate accuracy. Furthermore, an adaptive variable step scheme is also incorporated into the BCNLMS algorithm to improve the convergence speed and stability during the process of adaptively frequency estimation. The experimental results show that the proposed BCVNLMS algorithm outperforms other well‐known methods for frequency estimation of the TPPS in several scenarios such as severe noise pollution, harmonic interference and frequency mutation. [ABSTRACT FROM AUTHOR]

• The kernel adaptive filter with generalized tanh function (KAF-GT) is proposed, which shows favourable performance in combating impulsive noise. • The convergence analysis of the proposed KAF-GT algorithm is conducted using energy conservation relation. • By using the pre-tuned dictionary strategy to model the unknown nonlinear system, we perform the EMSE analysis and arrive at a closed-form solution for predicting the steady-state behavior. • To further improve the performance of KAF-GT, an optimization scheme for scale factor is designed. Recently, online learning algorithms in machine learning have been imposed much attention. As a typical family, kernel adaptive filtering algorithms receive particular interest due to their sequential learning-based features. However, the kernel least mean square (KLMS) algorithm is not suitable for nonlinear tasks corrupted by non-Gaussian noise, especially impulsive noise. This is because the derivation of the KLMS algorithm is on the basis of the mean square error (MSE) criterion which only captures information of second-order statistics. In this paper, motivated by tanh function, we develop its generalized variant by introducing a scale factor for better representation capability; then we incorporate kernel adaptive filter with the generalized tanh function to propose a robust sequential learning algorithm. Based on establishing the energy conservation relation, we derive a sufficient condition for ensuring the algorithm convergence. In addition, to perform the steady-state excess mean square error (EMSE) analysis, we use the pre-tuned dictionary strategy to model the unknown nonlinear system in form of a finite-order combination; by Taylor expansion, we arrive at a closed-form solution for predicting the steady-state behavior. To further improve the algorithm performance, we design an optimization scheme for scale factor. Simulations for nonlinear time series prediction show that the designed schemes yield better performance than some state-of-art algorithms. The steady-state EMSE analysis is validated to provide accurate prediction results. [ABSTRACT FROM AUTHOR]

• An optimization problem about the state mean of multi-agent systems with impulsive noise based on MCC derived from information theoretic learning is investigated in this paper, where the half-quadratic(HQ) strategy is used to simplify the solving process of the optimization problem. • A problem interfered by impulsive noise on the states of multi-agent systems is studied in this paper, where the adverse effect of impulsive noise on the states is eliminated by the MCC algorithm. • An event-triggered consensus problem of discrete-time multi-agent systems with random impulsive noise is addressed in this paper, where an event-triggering condition is derived for the system to achieve consensus. An event-triggered consensus problem of discrete-time multi-agent systems with impulsive noise under directed switching topology is investigated in this paper, where the control input of each agent is determined by its own state information and the state information of its neighbors that may be corrupted by impulsive noise. Firstly, to reduce the adverse effect of impulsive noise on consensus performance of the discrete-time multi-agent systems, maximum correntropy criterion (MCC) derived from information theoretic learning is introduced to calculate the communication weights among the agents, and an event-triggered strategy is adopted to drive the state variables of each agent to eventually converge to a same value. Secondly, the condition to guarantee consensus of the discrete-time multi-agent systems is derived by graph theory and Lyapunov stability theorem. Finally, the effectiveness of the theory is verified by several numerical simulations. [ABSTRACT FROM AUTHOR]

• A novel spatial attention encoder is proposed, which builds global feature insight with long-range dependence to enhance perception. • We propose a context block decoder to aggregate contextual information as channel-specific bias of the features. • A feature fusion module with parallel branches considering expectation and residual property is proposed to realize adaptive information fusion. • We build an ESD dataset with 89.70% small defects collected from real industry an evaluation benchmark. Despite the promising development of Automatic Visual Inspection (AVI) in the manufacturing industry, detecting small-sized defects with fewer pixels coverage remains a challenging problem due to its insufficient attention and lack of semantic information. Most exsiting convolutional inspection methods overlook the long-range dependence of context and lack adaptive fusion strategies to exploit heterogeneous features. To address these issues in AVI, this paper proposes a novel contextual information and spatial attention based network (CANet), which consists of two steps, namely CAblock and LaplacianFPN, for effective perception and exploitation of small defect features. Specifically, CAblock extracts semantic information with rich context by encoding spatial long-range dependence and decoding contextual information as channel-specific bias through a Spatial Attention Encoder (SAE) and a Context Block Decoder (CBD), respectively. LaplacianFPN further performs adaptive feature fusion considering both feature consistency and heterogeneity via two parallel branches. As a benchmark, a self-built Engine Surface Defects (ESD) dataset collected in real industry containing 89.70% small defects is constructed. Experimental results show that CANet achieves mAP-50 improvements of 1.5% and 4.3% compared to state-of-the-art methods on NEU-DET and ESD, which demonstrates the effectiveness of the proposed method. The code is now available at https://github.com/xiuqhou/CANet. [ABSTRACT FROM AUTHOR]

In order to efficiently utilize the information in the data and eliminate the negative effects of outliers in the principal component analysis (PCA) method, in this paper, we propose a novel robust sparse PCA method based on maximum correntropy criterion (MCC) with high-order manifold constraints called the RHSPCA. Compared with the traditional PCA methods, the proposed RHSPCA has the following benefits: 1) the MCC regression term is more robust to outliers than the MSE-based regression term; 2) thanks to the high-order manifold constraints, the low-dimensional representations can preserve the local relations of the data and greatly improve the clustering and classification performance for image processing tasks; and 3) in order to further counteract the adverse effects of outliers, the MCC-based samples’ mean is proposed to better centralize the data. We also propose a new solver based on the half-quadratic technique and accelerated block coordinate update strategy to solve the RHSPCA model. Extensive experimental results show that the proposed method can outperform the state-of-the-art robust PCA methods on a variety of image processing tasks, including reconstruction, clustering, and classification, on outliers contaminated datasets. [ABSTRACT FROM AUTHOR]

High-dimensional data contain not only redundancy but also noises produced by the sensors. These noises are usually non-Gaussian distributed. The metrics based on Euclidean distance are not suitable for these situations in general. In order to select the useful features and combat the adverse effects of the noises simultaneously, a robust sparse subspace learning method in unsupervised scenario is proposed in this paper based on the maximum correntropy criterion that shows strong robustness against outliers. Furthermore, an iterative strategy based on half quadratic and an accelerated block coordinate update is proposed. The convergence analysis of the proposed method is also carried out to ensure the convergence to a reliable solution. Extensive experiments are conducted on real-world data sets to show that the new method can filter out the outliers and outperform several state-of-the-art unsupervised feature selection methods. [ABSTRACT FROM AUTHOR]

Summary: Two novel adaptive filtering algorithms based on the mixed square/fourth error criterion are proposed for solving sparse system identification problems. Motivated by the fact that the proportionate update scheme can enhance the tracking ability of the system, we develop a proportionate least mean square/fourth (PLMS/F) algorithm in this paper. Combining the proportionate update scheme and the LMS/F algorithm, the proposed PLMS/F algorithm shows superiority for non‐Gaussian noise environments. Moreover, to further improve the performance of the PLMS/F algorithm in the noisy input cases, a bias‐compensated PLMS/F algorithm is developed by incorporating an unbiased criterion to compensate the bias caused by input noises. Simulation results in the context of the sparse system identification framework demonstrate that the proposed PLMS/F and bias‐compensated PLMS/F algorithms can achieve excellent identification performance in terms of steady‐state misalignment and convergence speed under noisy input and non‐Gaussian output noise environments. [ABSTRACT FROM AUTHOR]

Highlights • We propose a bias-compensated normalized MCC algorithm which is efficient for the noisy input and impulsive output noise cases. • We use the unbiasedness criterion to derive the bias compensated term for the proposed algorithm. • The proposed BCNMCC algorithm performs better than the original NMCC algorithm in noisy input environment. Abstract This paper proposes a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive output noise environment. The normalized maximum correntropy criterion (NMCC) is derived from a correntropy based cost function, which is rather robust with respect to impulsive noises. To deal with the noisy input, we introduce a bias-compensated vector to the NMCC algorithm, and then an unbiasedness criterion and some reasonable assumptions are used to compute the bias-compensated vector. Taking advantage of the bias-compensated vector, the bias caused by the input noise can be effectively suppressed. System identification simulation results demonstrate that the proposed BCNMCC algorithm can outperform other related algorithms with noisy input especially in an impulsive output noise environment. [ABSTRACT FROM AUTHOR]

• The UKF with EKRSL is developed by using the fixed-point iteration; • An enhanced EKRSL-UKF is proposed via an exponential function of innovation; • The proposed EnEKRSL-UKF is used for robust DSE. The traditional unscented Kalman filter (UKF) with mean square error (MSE) criterion for dynamic state estimation (DSE) is sensitive for unknown non-Gaussian noise and outliers. Leading to biased state estimates. This paper proposes a novel robust UKF with extended kernel risk-sensitive loss (EKRSL) for DSE considering unknown non-Gaussian process and measurement noises. Instead of MSE criterion, a novel robust EKRSL via the generalized Gaussian density is defined in KRSL framework, and we further develop a new robust UKF using the EnKRSL(called EKRSL-UKF). To obtain the recursive form of EKRSL-UKF, the statistical linear regression model is used and the fixed-point iteration is further utilized to iteratively get the optimal state estimate. An error constrained method is also introduced to restrict the error to address the numerical instability problem caused by large outliers. Furthermore, an enhanced EKRSL-UKF is established by using an exponential function of innovation to improve the estimation accuracy in the presence of noise uncertainties. Numerical results carried out on the IEEE 39-bus test system demonstrate that the proposed method can achieve desired robustness without loss of estimation accuracy under various conditions. [ABSTRACT FROM AUTHOR]

• A novel hypergraph based semi-supervised SNMF method is proposed for image clustering. • A new hypergraph based pairwise constraints propagation algorithm is presented. • The proposed method is analysed in terms of convergence, supervisory information, and computational complexity. • Extensive experiments confirm the effectiveness of the proposed method in clustering applications. Semi-supervised symmetric nonnegative matrix factorization (SNMF) has been shown to be a significant method for both linear and nonlinear data clustering applications. Nevertheless, existing SNMF-based methods only adopt a simple graph to construct the similarity matrix, and cannot fully use the limited supervised information for the construction of the similarity matrix. To overcome the drawbacks of previous SNMF-based methods, a new semi-supervised SNMF-based method called hypergraph based semi-supervised SNMF (HSSNMF), is proposed in this paper for image clustering. Specifically, HSSNMF adopts a predefined hypergraph to build a similarity matrix for capturing the high-order relationships of samples. By exploiting a new hypergraph based pairwise constraints propagation (HPCP) algorithm, HSSNMF propagates the pairwise constraints of the limited data points to the entire data points, which can make full use of the limited supervised information and construct a more informative similarity matrix. Using the multiplicative updating algorithm, a discriminative assignment matrix can then be obtained by solving the optimization problem of HSSNMF. Moreover, analyses of the convergence, supervisory information, and computational complexity of HSSNMF are presented. Finally, extensive clustering experiments have been conducted on six real-world image datasets, and the experimental results have demonstrated the superiority of HSSNMF while compared with several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Modern technologies have been producing data with complex intrinsic structures, which can be naturally represented as two-dimensional matrices, such as gray digital images, and electroencephalography (EEG) signals. When processing these data for classification, traditional classifiers, such as support vector machine (SVM) and logistic regression, have to reshape each input matrix into a feature vector, resulting in the loss of structural information. In contrast, modern classification methods such as support matrix machine capture these structures by regularizing the regression matrix to be low-rank. These methods assume that all entities within each input matrix can serve as the explanatory features for its label. However, in real-world applications, many features are redundant and useless for certain classification tasks, thus it is important to perform feature selection to filter out redundant features for more interpretable modeling. In this paper, we tackle this issue, and propose a novel classification technique called Sparse Support Matrix Machine (SSMM), which is favored for taking both the intrinsic structure of each input matrix and feature selection into consideration simultaneously. The proposed SSMM is defined as a hinge loss for model fitting, with a new regularization on the regression matrix. Specifically, the new regularization term is a linear combination of nuclear norm and ℓ 1 norm, to consider the low-rank property and sparse property respectively. The resulting optimization problem is convex, and motivates us to propose a novel and efficient generalized forward-backward algorithm for solving it. To evaluate the effectiveness of our method, we conduct comparative studies on the applications of both image and EEG data classification problems. Our approach achieves state-of-the-art performance consistently. It shows the promise of our SSMM method on real-world applications. [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM) is a popular learning algorithm for single hidden layer feedforward networks (SLFNs). It was originally proposed with the inspiration from biological learning and has attracted massive attentions due to its adaptability to various tasks with a fast learning ability and efficient computation cost. As an effective sparse representation method, orthogonal matching pursuit (OMP) method can be embedded into ELM to overcome the singularity problem and improve the stability. Usually OMP recovers a sparse vector by minimizing a least squares (LS) loss, which is efficient for Gaussian distributed data, but may suffer performance deterioration in presence of non-Gaussian data. To address this problem, a robust matching pursuit method based on a novel kernel risk-sensitive loss (in short KRSLMP) is first proposed in this paper. The KRSLMP is then applied to ELM to solve the sparse output weight vector, and the new method named the KRSLMP-ELM is developed for SLFN learning. Experimental results on synthetic and real-world data sets confirm the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The adaptive variable step-size technique is an effective way to improve the convergence rate of an adaptive filtering algorithm. However, for most existing variable step-size sign algorithm against impulsive noise, either the performance is not good enough, or the calculation is complex. In this paper, a correntropy inspired variable step-size method for sign algorithm is proposed. The new variable step-size method is computationally simple and robust to impulsive noises. Theoretical analysis and simulation results demonstrate that the proposed algorithm can achieve desirable performance with low computational complexity in presence of impulsive noises. [ABSTRACT FROM AUTHOR]

In hyperspectral images, some spectral bands suffer from low signal-to-noise ratio due to noisy acquisition and atmospheric effects, thus requiring robust techniques for the unmixing problem. This paper presents a robust supervised spectral unmixing approach for hyperspectral images. The robustness is achieved by writing the unmixing problem as the maximization of the correntropy criterion subject to the most commonly used constraints. Two unmixing problems are derived: the first problem considers the fully constrained unmixing, with both the nonnegativity and sum-to-one constraints, while the second one deals with the nonnegativity and the sparsity promoting of the abundances. The corresponding optimization problems are solved using an alternating direction method of multipliers (ADMM) approach. Experiments on synthetic and real hyperspectral images validate the performance of the proposed algorithms for different scenarios, demonstrating that the correntropy-based unmixing with ADMM is particularly robust against highly noisy outlier bands. [ABSTRACT FROM PUBLISHER]

This paper investigates the disturbance observer-based composite fuzzy control of a class of uncertain nonlinear systems with unknown dead zone. With fuzzy logic system approximating the unknown nonlinearities, composite learning is constructed on the basis of a serial–parallel identifier. By introducing the intermediate signal, the disturbance observer is developed to provide efficient learning of the compounded disturbance which includes the effect of time-varying disturbance, fuzzy approximation error, and unknown dead zone. Based on the disturbance estimation and fuzzy approximation, the adaptive fuzzy controller is synthesized with novel updating law. The stability analysis of the closed-loop system is rigorously established via Lyapunov approach. The performance of the proposed controller is verified via simulation that faster convergence and higher precision are obtained. [ABSTRACT FROM PUBLISHER]