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Considering the fact that it is very difficult to fully model an autonomous underwater vehicle (AUV) in the complex water environment, this paper presents a model-free tracking control strategy for an AUV in the presence of unknown disturbances. We first formulate an optimized control problem by defining a tracking Hamilton–Jacobi–Isaac (HJI) equation. Then, we present a reinforcement learning (RL) algorithm to compute an optimized solution by learning from the HJI equation online. It is noted that during the learning period, no information about the AUV's dynamics is needed. In order to demonstrate the efficiency of the proposed strategy, numerical simulation is considered, results are validated and discussed. [ABSTRACT FROM AUTHOR]

• Various types of robustness used in RL and control communities are classified. • The concept of safety robustness of RL policies is proposed. • A general algorithm of approximating the safety robustness of policies is proposed. • Experiments are conducted to demonstrate how the proposed algorithm works. For a reinforcement learning (RL) problem without a specified reward function, one may specify different reward functions to better guide an agent to learn. With different reward functions, the agent can learn different policies that generally have different robustness. Both the achieved reward and the success rate have been commonly used to evaluate the robustness of policies. Safety is a concern when using RL to solve problems in many safety–critical applications (e.g., robotic manipulation). However, evaluating the robustness of policies from the perspective of safety has not been discussed in the literature. The major contributions of this paper are the proposal of a novel concept of safety robustness to evaluate the robustness of policies from the perspective of safety and an algorithm to approximate the safety robustness of policies. To demonstrate how to implement the proposed algorithm, illustrative experiments are conducted and the safety robustness of three policies for controlling the manipulation of a cable-driven parallel robot is analyzed. Experiment results show that the proposed algorithm can approximate the safety robustness of policies using the ratio of the number of safe episodes to the number of total episodes and identify the best policy from multiple policies in terms of the safety of policies. [ABSTRACT FROM AUTHOR]

In this paper, a hierarchical method for human body segmentation in static images is proposed. Based on the face detection, a torso model is developed to locate the torso to provide the foreground seeds for torso segmentation with graph cuts. Similarly, an upper leg model is designed for lower body segmentation based on the segmented torso. Besides, a general method which combines shape information into graph cuts is presented, which can make the segmentation more accurate. The main contributions of this paper include two models and a new method of integrating shape information into graph cuts. Experiments on our collected real world images show that our method can accurately recover human body from static images with a variety of individuals, poses, backgrounds and clothing. [ABSTRACT FROM AUTHOR]

Abstract RANSAC is a popular robust model estimation algorithm in various computer vision applications. However, the speed of RANSAC declines dramatically as the inlier rate of the measurements decreases. In this paper, a novel Adaptively Ranked Sample Consensus(ARSAC) algorithm is presented to boost the speed and robustness of RANSAC. The algorithm adopts non-uniform sampling based on the ranked measurements to speed up the sampling process. Instead of a fixed measurement ranking, we design an adaptive scheme which updates the ranking of the measurements, to incorporate high quality measurements into sample at high priority. At the same time, a geometric constraint is proposed during sampling process to select measurements with scattered distribution in images, which could alleviate degenerate cases in epipolar geometry estimation. Experiments on both synthetic and real-world data demonstrate the superiority in efficiency and robustness of the proposed algorithm compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Dimensionality reduction as one of most attractive topics in machine learning research area has aroused extensive attentions in recent years. In order to preserve the local structure of data, most of dimensionality reduction methods consider constructing the relationships among each sample and its k nearest neighbors, and they find the neighbors in original space by using Euclidean distance. Since the data in original space contain some noises and redundant features, finding the neighbors in original space is incorrect and may degrade the subsequent performance. Therefore, how to find the optimal k nearest neighbors for each sample is the key point to improve the robustness of model. In this paper, we propose a novel dimensionality reduction method, named Adaptive Neighborhood MinMax Projections (ANMMP) which finds the neighbors in the optimal subspace by solving Trace Ratio problem in which the noises and redundant features have been removed already. Meanwhile, the samples within same class are pulled together while the samples between different classes are pushed far away in such learned subspace. Besides, proposed model is a general approach which can be implemented easily and applied on other methods to improve the robustness. Extensive experiments conducted on several synthetic data and real-world data sets and achieve some encouraging performance with comparison to metric learning and feature extraction methods, which demonstrates the efficiency of our method. [ABSTRACT FROM AUTHOR]

Correntropy is a local similarity measure between two arbitrary variables, and it has been applied in a variety of learning algorithms to improve noise insensitivity. In this paper, based on the correntropy, a non-convex and bounded loss function is obtained which contains second and higher order moments of the classification margin. And the novel loss function is robust to noises and close to the 0–1 loss function. Then we introduce it into extreme learning machine (ELM), and propose a correntropy-based robust ELM framework for classification, trained by half quadratic optimization to cope with non-convexity of the algorithm. To evaluate robustness, feature noise and label noise are simulated to provide noisy environments. Experimental results on benchmark datasets demonstrate that the proposed algorithm is better than original algorithms and robust algorithms. Moreover, the superiority of proposed algorithm in noisy environment is more evident, which further proves its robustness to noises. [ABSTRACT FROM AUTHOR]

Since principal component analysis (PCA) is sensitive to corrupted variables or observations that affect its performance and applicability in real scenarios, some convex robust PCA methods have been developed to enhance the robustness of PCA. However, most of them neglect the optimal mean calculation problem. They center the data with the mean calculated by the ℓ 2 -norm, which is incorrect because the ℓ 1 -norm objective function is used in the following steps. In this paper, we consider a novel robust PCA method that can pursue and remove outliers, exactly recover a low-rank matrix and calculate the optimal mean. Specifically, we propose an optimization model constituted by a ℓ 2,1 -norm based loss function and a Schatten p -norm regularization term. The ℓ 2,1 -norm used in loss function aims to pursue and remove outliers, the Schatten p -norm can suppress the singular values of reconstructed data at smaller p (0 <  p  < 1), so it is a better approximation to the rank than the trace norm. Experimental results on benchmark databases demonstrate the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

There are two categories of partial shape matching problem: whole-to-part matching and part-to-part matching. The first establishes the best match between an open curve and a part of a closed curve, while the second is to find a part of the first input closed curve that can be well aligned with a part of the second input closed curve. In this paper, we present a novel approach to solve these two categories of the partial shape matching problem. First, we propose a novel shape descriptor, triangular centroid distances (TCDs), for shape representation; the TCDs shape descriptor is invariant to translation, rotation, scaling, and considerable shape deformations. Then, using the TCDs shape descriptor, we present a method to deal with the whole-to-part partial shape matching problem. Finally, we extend our work to part-to-part partial shape matching. Here, we propose a new approach, again using the TCDs shape descriptor, to solve the part-to-part partial shape matching problem. Experimental results demonstrate that our method outperforms existing methods in 2D nonrigid partial shape matching. [ABSTRACT FROM AUTHOR]

As having the closed form solution, the least squares support vector machine (LSSVM) has been widely used for classification and regression problems owing to its competitive performance compared with other types of SVMs. However, the LSSVM has two drawbacks: it is sensitive to outliers and its solution lacks sparseness. The robust LSSVM (R-LSSVM) partially overcomes the first drawback via its nonconvex truncated loss function, but it is unable to address the second drawback because its current algorithms produce dense solutions that are inefficient for training large-scale problems. In this paper, we interpret the robustness of the R-LSSVM from a re-weighted viewpoint and develop a primal R-LSSVM using the representer theorem. The new model may have a sparse solution. Then, we design a convergent sparse R-LSSVM (SR-LSSVM) algorithm to achieve a sparse solution of the primal R-LSSVM after obtaining a low-rank approximation of the kernel matrix. The new algorithm not only overcomes the two drawbacks of LSSVM simultaneously, it also has lower complexity than the existing algorithms. Therefore, it is very efficient at training large-scale problems. Numerous experimental results demonstrate that the SR-LSSVM can achieve better or comparable performance to other related algorithms in less training time, especially when used to train large-scale problems. [ABSTRACT FROM AUTHOR]

Word embeddings and CNN (convolutional neural networks) architecture are crucial ingredients of sentiment analysis. However, sentiment and lexicon embeddings are rarely used and CNN is incompetent to capture global features of sentence. To this end, semantic embeddings, sentiment embeddings and lexicon embeddings are applied for texts encoding, and three different attentions including attention vector, LSTM (long short term memory) attention and attentive pooling are integrated with CNN model in this paper. Additionally, a word and its context are explored to disambiguate the meaning of the word for rich input representation. To improve the performance of three different attention CNN models, CCR (cross-modality consistent regression) and transfer learning are presented. It is worth noticing that CCR and transfer learning are used in textual sentiment analysis for the first time. Finally, some experiments on two different datasets demonstrate that the proposed attention CNN models achieve the best or the next-best results against the existing state-of-the-art models. [ABSTRACT FROM AUTHOR]

Freehand three-dimensional (3D) ultrasound imaging is an attractive research area because it is capable of providing large field of view and high in-plane resolution image to allow better illustration of complex anatomy structures. However, reconstructed image is corrupted with speckle noise and artifacts in the conventional reconstructed volume data. In this paper, we propose a simple but effective adaptive kernel regression method for volume reconstruction from freehand swept B-scan images. By creating a linear model for estimating the homogeneous region of the B-scan image and learning the parameters of the model with a supervised learning method, the statistical characteristic of speckle can be well recovered. With the learned linear model of speckle, we can easily estimate the homogenous region and reconstruct image with speckle reduction and edge preservation via the adaptive turning of the smoothing parameters of the kernel regression. Our algorithm lends itself to parallel processing, and yields a 288× speedup on a graphics processing unit (GPU). Experiments on the simulated data, ultrasonic abdominal phantom and in-vivo liver of human subject and comparisons with some classical and recent algorithms are used to demonstrate its improvements in both volume reconstruction accuracy and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, the problem of event-triggered output feedback synchronization control for complex dynamical networks (CDN) is studied. Based on a distributed state observer, a novel event-triggered mechanism is firstly introduced to design the output feedback synchronization controller, and by applying a useful graph theoretical result, the asymptotic convergence of the synchronization errors is then guaranteed. Moreover, it is proved that the designed event-triggered synchronization method is also valid for the CDNs with time-varying coupling weights. In particular, the proposed method is an extension of the existing state feedback event-triggered synchronization results. Finally, the circuit realizations for the new event-triggered mechanism and control approach are given, and the effectiveness of the proposed scheme is illustrated through the simulation results. [ABSTRACT FROM AUTHOR]

In big data fields, with increasing computing capability, artificial neural networks have shown great strength in solving data classification and regression problems. The traditional training of neural networks depends generally on the error back propagation method to iteratively tune all the parameters. When the number of hidden layers increases, this kind of training has many problems such as slow convergence, time consuming, and local minima. To avoid these problems, neural networks with random weights (NNRW) are proposed in which the weights between the hidden layer and input layer are randomly selected and the weights between the output layer and hidden layer are obtained analytically. Researchers have shown that NNRW has much lower training complexity in comparison with the traditional training of feed-forward neural networks. This paper objectively reviews the advantages and disadvantages of NNRW model, tries to reveal the essence of NNRW, gives our comments and remarks on NNRW, and provides some useful guidelines for users to choose a mechanism to train a feed-forward neural network. [ABSTRACT FROM AUTHOR]

This paper presents a novel unsupervised multi-source domain adaptation approach, named as coupled local–global adaptation (CLGA). At the global level, in order to maximize the adaptation ability, CLGA regards multiple domains as a unity, and jointly mitigates the gaps of both marginal and conditional distributions between source and target dataset. At the local level, with the intention of maximizing the discriminative ability, CLGA investigates the relationship among distinctive domains, and exploits both class and domain manifold structures embedded in data samples. We formulate both local and global adaptation in a concise optimization problem, and further derive an analytic solution for the objective function. Extensive evaluations verify that CLGA performs better than several existing methods not only in multi-source adaptation tasks but also in single source scenarios. [ABSTRACT FROM AUTHOR]

In this paper, the dynamics including boundedness and stability for a general class of complex-valued neural networks with variable coefficients and proportional delays are investigated. By employing inequality techniques and mathematical analysis method, some sufficient criteria to guarantee boundedness and global exponential stability are established for the considered neural networks. As a special case that the coefficients of networks are constants, sufficient criteria are also derived to guarantee the existence, uniqueness and global exponential stability of the equilibrium point. This work generalizes and improves previously known results, and the obtained criteria can be tested and applied easily in practice. An illustrative example demonstrates the feasibility of the proposed results. [ABSTRACT FROM AUTHOR]

Autonomous road detection and modeling play a key role for UGVs navigating in complex outdoor environments. This paper investigates road detection and description for UGVs in various outdoor scenes under different weather conditions. A novel environment perception system that includes two cameras and multiple laser range finders is introduced firstly. Taking classification accuracy and time-cost into account, 8-dimensional features are selected from a 91-dimensional candidate feature set using Adaboost algorithm. To adapt to the diversity of road scenes under different weather conditions in different seasons, an online classifier based on SVM is proposed to replace the fixed one. Moreover, a road region adjusting algorithm is present to eliminate misclassified regions especially when the roads have fuzzy boundaries or obstacles. Finally, a RANSAC spline fitting algorithm is adopted to provide an accurate road border model for UGVs’ autonomous path planning and navigation. A series of experiments are conducted by using a self-built UGV platform and experimental results show the validity and practicality of the proposed approaches. [ABSTRACT FROM AUTHOR]

In this paper, the optimal performance of multi-input and multi-output (MIMO) linear time-invariant (LTI) discrete-time networked control systems (NCSs) are investigated based on state-space representation. In communication channels, channel fading, additive white Gaussian noise (AWGN) and scaling parameter matrix are considered. An average performance index is constructed. Moreover, the control energy and the output energy of the system are considered simultaneously in the output regulation performance. The scaling parameter matrix is introduced, which may be considered as an encoder or modification on the channel. The optimal regulation performances of the system are obtained, and the relationship is given between the optimal regulation performances and the steady-state solution of the corresponding algebraic Riccati equation (ARE). Results indicate that the optimal performance is influenced by the non-minimum phase zeros, unstable poles, the scaling parameter matrix, and the characteristics of the communication noise and fading. In addition, the channel power constraint is considered in Theorem 2 . We introduce a novel power weighting factor, which can weigh the relative importance of system output and channel input energy. The obtained results will give some guidance for the design of NCSs. Finally, a simulation example is given to demonstrate effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Compared to studies of analyzing hyperspectral image (HSI), finding spectral rectification, especially the seriously distorted pixels in boundaries, was seldom addressed in a clear way, albeit of first importance in HSI analysis and interpretation. In this paper, we present a simple but promising rectification method works in a coarse-to-fine framework for removing noise and enhancing useful features. Our approach, called CSR (coarse-to-fine spectral rectification), combines the theory of scale-aware with local smoothness for HSI rectification problem that is seldom pointed out. The useless information like noise in small scale is removed firstly. Then, the distinctive information like boundary in large scale is enhanced. The experimental result enjoys a built-in smoothing effect and a fact of the identical materials with same or similar signatures, which is suited for HSI subsequent application. Furthermore, our approach has powerful influence on both classification via five classifiers in terms of class labeled data and unmixing regarding to class unlabeled data. Rectified by a coarse-to-fine framework, our method presents superior performance and runs much faster than the competing methods. [ABSTRACT FROM AUTHOR]

Motivated by the recent growing interest in pairwise learning problems, we study the generalization performance of Online Pairwise lEaRning Algorithm (OPERA) in a reproducing kernel Hilbert space (RKHS) without an explicit regularization. The convergence rates established in this paper can be arbitrarily closed to O ( T − 1 2 ) within T iterations and largely improve the existing convergence rates for OPERA. Our novel analysis is conducted by showing an almost boundedness of the iterates encountered in the learning process with high probability after establishing an induction lemma on refining the RKHS norm estimate of the iterates. [ABSTRACT FROM AUTHOR]

One of the most active areas in computer vision is object detection, which has made significant improvement in recent years. Current state-of-the-art object detection methods mostly adhere to the framework of the regions with convolutional neural network (R-CNN). However, they only take advantage of the local appearance features inside object bounding boxes. Since these approaches ignore the contextual information around the object proposals, the outcome of these detectors may generate a semantically incoherent interpretation of the input image. In this paper, we propose a novel object detection system which incorporates the local appearance and the contextual information. Specifically, the contextual information comprises the relationships among objects and the global scene based contextual feature generated by a convolutional neural network. The whole system is formulated as a fully connected conditional random field (CRF) defined on object proposals. Then the contextual constraints among object proposals are modeled as edges naturally. Furthermore, a fast mean field approximation method is utilized to infer in this CRF model efficiently. The experimental results demonstrate that our algorithm achieves a higher mean average precision (mAP) on PASCAL VOC 2007 datasets compared with the baseline algorithm Faster R-CNN. [ABSTRACT FROM AUTHOR]

In this paper, a novel multilayer backpropagation (BP) neural network model is proposed based on conjugate gradient (CG) method with generalized Armijo search. The presented algorithm requires low memory and performs fast convergent speed in practical applications. One reason is that the constructed conjugate direction guarantees the sufficient descent behavior in minimizing the given objective function. The other stems from the fact that the generalized Armijo method can automatically determine a more suitable learning rate in each training epoch. As a theoretical contribution, two deterministic convergent results, weak and strong convergence, have been detailedly proved under more relaxed assumptions. The weak convergence means that the norm of gradient of the objective function tends to zero. For the strong convergence, it represents that the sequence of weight vectors approaches a fixed point. To support the theoretical results, some illustrated simulations have been done on various benchmark datasets. [ABSTRACT FROM AUTHOR]

Generalized bilinear model (GBM) has been one of the most representative models for nonlinear unmixing of hyperspectral image (HSI), which can take the second-order scattering of photons into consideration. However, the GBM is implicitly developed for the additive white Gaussian noise. Besides, the performances of traditional GBM based unmixing methods are not that satisfying since the spatial correlation of HSI is not considered. In this paper, to overcome the two problems mentioned above, we propose a robust GBM (RGBM) for nonlinear unmixing of HSI, which can simultaneously take the Gaussian noise and sparse noise into account. Besides, we propose a new unmixing method with superpixel segmentation (SS) and low-rank representation (LRR) based on RGBM, which can take the spatial correlation of HSI into consideration. First, we adopt the principal component analysis (PCA) to get the first principal component of HSI, which contains the most information for the whole HSI. Then we adopt the SS in the first principal component of HSI to get the homogeneous regions, and the abundances in each homogeneous region have the underlying low-rank property. Finally, we unmix the pixels in each homogeneous region of HSI according to the low-rank property of abundances and the sparse property of sparse noise, and the proposed RGBM based unmixing method can be solved by the alternative direction method of multipliers (ADMM). Experiments on both synthetic datasets and real HSIs demonstrate that the proposed RGBM and corresponding method are efficient compared with some other popular GBM based unmixing methods. [ABSTRACT FROM AUTHOR]

Clustering analysis has been applied in all aspects of data mining. Density-based and grid-based clustering algorithms are used to form clusters from the core points or dense grids to extend to the boundary of the clusters. However, deficiencies are still existed. To find out the right boundary and improve the precision of the cluster, this paper has proposed a new clustering algorithm (named C-USB) based on the skew characteristic of the data distribution in the cluster margin region. The boundary degree calculated by skew degree and the local density are used to distinguish whether a data is an internal point or non-internal point. And the connected matrix is constructed by removing the neighbor relationships of non-internal points from the relationships of all points, then the clusters can be formed by searching from the connected matrix towards internal of the clusters. Experimental results on synthetic and real data sets show that the C-USB has higher accuracy than that of similar algorithms. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of input-to-state stability (ISS) for a class of switched impulsive memristive neural networks with stochastic disturbance. Based on the multiple Lyapunov functions method and comparison principle, new stability results for such kind of systems are derived. The cases that all subsystems are unstable and both stable/unstable subsystems coexist are considered, respectively. Time-varying delays are taken into account in the stability analysis. The mean-square ISS delay-independent sufficient conditions are presented. Finally, numerical examples are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we improve the minimum squared error (MSE) algorithm for classification by modifying its classification rule. Differing from the conventional MSE algorithm which first obtains the mapping that can best transform the training sample into its class label and then exploits the obtained mapping to predict the class label of the test sample, the modified minimum squared error classification (MMSEC) algorithm simultaneously predicts the class labels of the test sample and the training samples nearest to it and combines the predicted results to ultimately classify the test sample. Besides this paper, for the first time, proposes the idea to take advantage of the predicted class labels of the training samples for classification of the test sample, it devises a weighted fusion scheme to fuse the predicted class labels of the training sample and test sample. The paper also interprets the rationale of MMSEC. As MMSEC generalizes better than conventional MSE, it can lead to more robust classification decisions. The face recognition experiments show that MMSEC does obtain very promising performance. [Copyright &y& Elsevier]

Abstract: The output weights computing of extreme learning machine (ELM) encounters two problems, the computational and outlier robustness problems. The computational problem occurs when the hidden layer output matrix is a not full column rank matrix or an ill-conditioned matrix because of randomly generated input weights and biases. An existing solution to this problem is Singular Value Decomposition (SVD) method. However, the training speed is still affected by the large complexity of SVD when computing the Moore–Penrose (MP) pseudo inverse. The outlier robustness problem may occur when the training data set contaminated with outliers then the accuracy rate of ELM is extremely affected. This paper proposes the Extended Complete Orthogonal Decomposition (ECOD) method to solve the computational problem in ELM weights computing via ECODLS algorithm. And the paper also proposes the other three algorithms, i.e. the iteratively reweighted least squares (IRWLS-ELM), ELM based on the multivariate least-trimmed squares (MLTS-ELM), and ELM based on the one-step reweighted MLTS (RMLTS-ELM) to solve the outlier robustness problem. However, they also encounter the computational problem. Therefore, the ECOD via ECODLS algorithm is also used successfully in the three proposed algorithms. The experiments of regression problems were conducted on both toy and real-world data sets. The outlier types are one-sided and two-sided outliers. Each experiment was randomly contaminated with outliers, of one type only, with 10%, 20%, 30%, 40%, and 50% of the total training data size. Meta-metrics evaluation was used to measure the outlier robustness of the proposed algorithms compared to the existing algorithms, i.e. the minimax probability machine regression (MPMR) and the ordinary ELM. The experimental results showed that ECOD can effectively replace SVD. The ECOD is robust to the not full column rank or the ill-conditional problem. The speed of the ELM training using ECOD is also faster than the ordinary training algorithm. Moreover, the meta-metrics measure showed that the proposed algorithms are less affected by the increasing number of outliers than the existing algorithms. [Copyright &y& Elsevier]

One key challenge of face detection is the large appearance variations due to some real-world factors, such as viewpoint, extreme illuminations and expression changes, which lead to the large intra-class variations and making the detection algorithm is not robust enough. In this paper, we propose a locality-sensitive support vector machine using kernel combination (LS-KC-SVM) algorithm to solve the above two problems. First, we employ the locality-sensitive SVM (LSSVM) to construct a local model on each local region, which can handle the classification task easier due to smaller within-class variation. Second, motivated by the idea that local features are more robust compared with global features, we use multiple local CNNs to jointly learn local facial features because of the powerful strength of CNN learning characteristic. In order to use this property of local features effectively, we apply the global and local kernels to the features and introduce the combination kernel to the LSSVM. Extensive experiments demonstrate the robustness and efficiency of our algorithm by comparing it with several popular face detection algorithms on the widely used CMU+MIT dataset and FDDB dataset. [ABSTRACT FROM AUTHOR]

This paper proposes a new robust reversible data hiding scheme for H.264. The embedded data is first distributed into n sub-secrets with matrix equation by using Shamir’s (t, n)-threshold secret sharing to improve the robustness of the embedded data. Then we choose the block with prediction mode and embed the sub-secrets into the coefficients of the 4×4 discrete cosine transform (DCT) block of the selected frames which meet our conditions to avert the distortion drift. The experimental results show that this new robust reversible data hiding algorithm can get more robustness, effectively avert intra-frame distortion drift and get good visual quality. [ABSTRACT FROM AUTHOR]

This paper studies subspace based multi-view learning, investigating how to mine useful information within various kinds of data or features (views) and achieve the optimal cooperation between views. Unlike most existing methods focused on learning an optimal weighting scheme to linearly combine different types of view information, we propose to first improve the original information provided by each view by designing a voting based scheme to model individual neighbor structures of the data. This leads to a set of refined local proximity matrices corresponding to different confidence levels. Then, different schemes can be applied to further combine this refined set of composite local neighborhood representations. Also, we provide the semi-supervised version of the proposed algorithms to incorporate partially labeled objects. The experimental results demonstrate effectiveness and robustness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The generation of fuzzy rules from samples for fuzzy modeling and control is significant. If samples contain noise and outliers, the Wang–Mendel (WM) method may lead to the extraction of invalid rules resulting in low confidence of the rules. The scale of the samples also affects the efficiency of the WM method. Interaction among input variables can help the WM method achieve high completeness and robustness. The fuzzy C-means clustering (FCM) algorithm can reduce the scale of samples and undo noisy data to some degree. This paper aims to develop an FCM-based improved WM method that adopts a modified FCM algorithm to preprocess the original samples and compute the interaction among the samples. Then, the optimized samples are used to generate fuzzy rules, thereby building a complete rule set through extrapolation. Experimental results from two nonlinear functions and short-term load forecasting case study show that the proposed method not only has high completeness and robustness, but also ensures better prediction accuracy of the fuzzy system. [ABSTRACT FROM AUTHOR]

This paper presents a robust readable H.264/AVC data hiding algorithm without intra-frame distortion drift. We first divide the original embedded data into several groups by using the secret sharing technique. Then we use the BCH syndrome code (BCH code) technique to encode each grouping of data. Finally, we embed the encoded data into the paired-coefficients of the 4×4 discrete cosine transform (DCT) block of the selected frames which meet our conditions to avert the distortion drift. The experimental results show that this new data hiding algorithm can get more robustness, effectively avert intra-frame distortion drift and get high visual quality. [ABSTRACT FROM AUTHOR]

This paper proposes a robust readable reversible data hiding scheme in H.264/AVC. We encode the embedded data using BCH syndrome code (BCH) before data hiding to improve robustness, then we embed the encoded data into the quantized discrete cosine transform (DCT) coefficients of the 4×4 blocks of I frames which meet the directions of intra-frame prediction, and the directions of intra-frame prediction are utilized to avert the distortion drift. The experimental results show that this new robust reversible data hiding algorithm can get more robustness, effectively avert intra-frame distortion drift and get good visual quality. [ABSTRACT FROM AUTHOR]

Abstract: Least squares support vector machines (LS-SVMs) are sensitive to outliers or noise in the training dataset. Weighted least squares support vector machines (WLS-SVMs) can partly overcome this shortcoming by assigning different weights to different training samples. However, it is a difficult task for WLS-SVMs to set the weights of the training samples, which greatly influences the robustness of WLS-SVMs. In order to avoid setting weights, in this paper, a novel robust LS-SVM (RLS-SVM) is presented based on the truncated least squares loss function for regression and classification with noise. Based on its equivalent model, we theoretically analyze the reason why the robustness of RLS-SVM is higher than that of LS-SVMs and WLS-SVMs. In order to solve the proposed RLS-SVM, we propose an iterative algorithm based on the concave–convex procedure (CCCP) and the Newton algorithm. The statistical tests of the experimental results conducted on fourteen benchmark regression datasets and ten benchmark classification datasets show that compared with LS-SVMs, WLS-SVMs and iteratively reweighted LS-SVM (IRLS-SVM), the proposed RLS-SVM significantly reduces the effect of the noise in the training dataset and provides superior robustness. [Copyright &y& Elsevier]

Abstract: This paper considers the problem of mixing matrix estimation in underdetermined blind source separation (UBSS). We propose a simple and effective detection algorithm which detects the time-frequency (TF) points occupied by only a single source for each source. The detection algorithm identifies the single source points by comparing the normalized real and imaginary parts of the TF coefficient vectors of the mixed signals, which is simpler than previously reported algorithms. Then we propose a modified similarity-based robust clustering method (MSCM) to estimate the number of sources and the mixing matrix using these detected single source points. Experimental results show the efficiency of the proposed algorithm, especially in the cases where the number of sources is unknown. [Copyright &y& Elsevier]

Abstract: This paper presents a novel algorithm for mobile-object segmentation from static background scenes, which is both robust and accurate under most of the common problems found in motion segmentation. In our first contribution, a case analysis of motion segmentation errors is presented taking into account the inaccuracies associated with different cues, namely colour, edge and intensity. Our second contribution is an hybrid architecture which copes with the main issues observed in the case analysis by fusing the knowledge from the aforementioned three cues and a temporal difference algorithm. On one hand, we enhance the colour and edge models to solve not only global and local illumination changes (i.e. shadows and highlights) but also the camouflage in intensity. In addition, local information is also exploited to solve the camouflage in chroma. On the other hand, the intensity cue is applied when colour and edge cues are not available because their values are beyond the dynamic range. Additionally, temporal difference scheme is included to segment motion where those three cues cannot be reliably computed, for example in those background regions not visible during the training period. Lastly, our approach is extended for handling ghost detection. The proposed method obtains very accurate and robust motion segmentation results in multiple indoor and outdoor scenarios, while outperforming the most-referred state-of-art approaches. [Copyright &y& Elsevier]

Abstract: Visual tracking aims to match objects of interest in consecutive video frames. This paper proposes a novel and robust algorithm to address the problem of object tracking. To this end, we investigate the fusion of state-of-the-art image segmentation hierarchies and graph matching. More specifically, (i) we represent the object to be tracked using a hierarchy of regions, each of which is described with a combined feature set of SIFT descriptors and color histograms; (ii) we formulate the tracking process as a graph matching problem, which is solved by minimizing an energy function incorporating appearance and geometry contexts; and (iii) more importantly, an effective graph updating mechanism is proposed to adapt to the object changes over time for ensuring the tracking robustness. Experiments are carried out on several challenging sequences and results show that our method performs well in terms of object tracking, even in the presence of variations of scale and illumination, moving camera, occlusion, and background clutter. [Copyright &y& Elsevier]

Abstract: Feature statistics normalization in the cepstral domain is one of the most performing approaches for robust automaticspeech and speaker recognition in noisy acoustic scenarios: feature coefficients are normalized by using suitable linear or nonlinear transformations in order to match the noisy speech statistics to the clean speech one. Histogram equalization (HEQ) belongs to such a category of algorithms and has proved to be effective on purpose and therefore taken here as reference. In this paper the presence of multi-channel acoustic channels is used to enhance the statistics modeling capabilities of the HEQ algorithm, by exploiting the availability of multiple noisy speech occurrences, with the aim of maximizing the effectiveness of the cepstra normalization process. Computer simulations based on the Aurora 2 database in speech and speaker recognition scenarios have shown that a significant recognition improvement with respect to the single-channel counterpart and other multi-channel techniques can be achieved confirming the effectiveness of the idea. The proposed algorithmic configuration has also been combined with the kernel estimation technique in order to further improve the speech recognition performances. [Copyright &y& Elsevier]

Abstract: In this paper, based on the previous results of deterministic learning, we investigate the performance of deterministic learning in noisy environments. Two different types of noises arising in practical implementations are considered: the system noise and the measurement noise. By employing the convergence results of a class of perturbed linear time-varying (LTV) systems, the effects of these noises upon the learning performance are revealed. It is shown that while there is little effect upon the learning speed, noises have much influence on the learning accuracy. Compared with system noise, the effects of measurement noise appear to be more complicated. Under the noisy environments, robustification technique on the learning algorithm is required to avoid parameter drift. Furthermore, it is shown that additive system noise can be used to enhance the generalization ability of the RBF networks. Simulation studies are included to illustrate the results. [Copyright &y& Elsevier]

Abstract: The local tangent space alignment (LTSA) has demonstrated promising results in finding meaningful low-dimensional structures hidden in high-dimensional data. However, LTSA may have a limited effectiveness on the data which are organized in multiple classes or contain noisy points. In this paper, the distances between the samples and their neighbors are rescaled by using the reconstruction weights to overcome the limitation. An extension of LTSA is proposed based on the local rescaled distance matrix. Numerical experiments on both synthetic and real-world data sets are used to show the improvement of our extension for classification and the robustness to noisy data. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to present (jointly) a series of robust high performance (award winning) implementations of reinforcement learning algorithms based on temporal-difference learning and weighted - nearest neighbors for linear function approximation. These algorithms, named methods, where rigorously tested at the Second and Third Annual Reinforcement Learning Competitions (RLC2008 and RCL2009) held in Helsinki and Montreal respectively, where the method (JAMH team) won in the PolyAthlon 2008 domain, obtained the second place in 2009 and also the second place in the Mountain-Car 2008 domain showing that it is one of the state of the art general purpose reinforcement learning implementations. These algorithms are able to learn quickly, to generalize properly over continuous state spaces and also to be robust to a high degree of environmental noise. Furthermore, we describe a derivation of algorithm for problems where the use of continuous actions have clear advantages over the use of fine grained discrete actions: the reinforcement learning algorithm. [Copyright &y& Elsevier]

Abstract: In this paper, we propose an object tracking framework based on a spatial pyramid heat kernel structural information representation. In the tracking framework, we take advantage of heat kernel structural information (HKSI) matrices to represent object appearance, because HKSI matrices perform well in characterizing the edge flow (or structural) information on the object appearance graph. To further capture the multi-level spatial layout information of the HKSI matrices, a spatial pyramid division strategy is adopted. Then, multi-scale HKSI subspace analysis is applied to each spatial pyramid grid at different levels. As a result, several grid-specific HKSI subspace models are obtained and updated by the incremental PCA algorithm. Based on the multi-scale grid-specific HKSI subspace models, we propose a tracking framework using a particle filter to propagate sample distributions over time. Theoretical analysis and experimental evaluations demonstrate the effectiveness of the proposed tracking framework. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a robust wavelet-based adaptive neural control (RWANC) with a PI type learning algorithm is proposed. The proposed RWANC system is composed of a wavelet neural controller and a fuzzy compensation controller. The wavelet neural control is utilized to approximate an ideal controller and the fuzzy compensation controller with a fuzzy logic system in it is used to remove the chattering phenomena of conventional sliding-mode control completely. In the RWANC, the learning algorithm is derived based on the Lyapunov function, thus the closed-loop system''s stability can be guaranteed. The chaotic system control has become an emerging topic in engineering community since the uncontrolled system displays complex, noisy-like and unpredictable behavior. Therefore, the proposed RWANC approach is applied to a second-order chaotic nonlinear system to investigate the effectiveness. Through the simulation results, the proposed RWANC scheme can achieve favorable tracking performance and the convergence of the tracking error and control parameters can be accelerated by the developed PI adaptation learning algorithm. [Copyright &y& Elsevier]

Abstract: This paper presents a grey–fuzzy predictive controller that is based on fuzzy theory, grey prediction and on-line switching algorithms. The grey predictor is applied to extract key information and reduce the randomness of the measured non-stationary time-series signals from sensors, and send the prediction information to the fuzzy controller. The complete mathematical model is derived and the sufficient condition for convergence is given. To achieve better transient performance and steady-state responses, an on-line switching mechanism is adopted to regulate appropriately the forecasting step size of the grey predictor, according to the error feedback from different periods of the system response. Experimental results obtained from a plant show that the control accuracy and robustness are much improved when the proposed new method is applied. [Copyright &y& Elsevier]

In this paper we extend the hierarchical self-organizing maps model (HSOM) to address the problem of learning topological drift under non-stationary and noisy environments. The new model, called robust and flexible hierarchical self-organizing maps (RoFlex-HSOM), combines the capabilities of robustness against noise and the flexibility to adapt to the changing environment. The RoFlex-HSOM model consists of a hierarchical tree structure of growing self-organizing maps (SOMs) that adapts its architecture based on the data. The model preserves the topology mapping from the high-dimensional time-dependent input space onto a neuron position in a low-dimensional hierarchical output space grid. Furthermore, the RoFlex-HSOM algorithm has the plasticity to track and adapt to the topological drift, it gradually forgets (but no catastrophically) previous learned patterns and it is resistant to the presence of noise. We empirically show the capabilities of our model with experimental results using synthetic sequential data sets and the “El Niño” real world data. [Copyright &y& Elsevier]

Abstract: Generalization ability of neural networks is very important and a rule of thumb for good generalization in neural systems is that the smallest system should be used to fit the training data. Unfortunately, it is normally difficult to determine the optimal size of networks, particularly, in the sequential training applications such as online control. In this paper, an online training algorithm with a dynamic pruning procedure is proposed for the online tuning and pruning the neural tracking control system. The conic sector theory is introduced in the design of this robust neural control system, which aims at providing guaranteed boundedness for both the input–output signals and the weights of the neural network. The proposed algorithm is applied to a multilayer perceptron with adjustable weights and a complete convergence proof is provided. The neural control system guarantees the closed-loop stability of the estimation, and in turn, a good tracking performance. The performance improvement of the proposed system over existing systems can be qualified in terms of better generalization ability, preventing weight shifts, fast convergence and robustness against system disturbance. [Copyright &y& Elsevier]