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Recently, sparse representation-based classification (SRC) has been widely studied and has produced state-of-the-art results in various classification tasks. Learning useful and computationally convenient representations from complex redundant and highly variable visual data is crucial for the success of SRC. However, how to find the best feature representation to work with SRC remains an open question. In this paper, we present a novel discriminative projection learning approach with the objective of seeking a projection matrix such that the learned low-dimensional representation can fit SRC well and that it has well discriminant ability. More specifically, we formulate the learning algorithm as a bilevel optimization problem, where the optimization includes an $\ell _{1}$ -norm minimization problem in its constraints. Through the bilevel optimization model, the relationship between sparse representation and the desired feature projection can be explicitly exploited during the learning process. Therefore, SRC can achieve a better performance in the transformed subspace. The optimization model can be solved by using a stochastic gradient ascent algorithm, and the desired gradient is computed using implicit differentiation. Furthermore, our method can be easily extended to learn a dictionary. The extensive experimental results on a series of benchmark databases show that our method outperforms many state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification applications directly, although variants of LS-SVM and PSVM have been proposed to handle such cases. This paper shows that both LS-SVM and PSVM can be simplified further and a unified learning framework of LS-SVM, PSVM, and other regularization algorithms referred to extreme learning machine (ELM) can be built. ELM works for the “generalized” single-hidden-layer feedforward networks (SLFNs), but the hidden layer (or called feature mapping) in ELM need not be tuned. Such SLFNs include but are not limited to SVM, polynomial network, and the conventional feedforward neural networks. This paper shows the following: 1) ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly; 2) from the optimization method point of view, ELM has milder optimization constraints compared to LS-SVM and PSVM; 3) in theory, compared to ELM, LS-SVM and PSVM achieve suboptimal solutions and require higher computational complexity; and 4) in theory, ELM can approximate any target continuous function and classify any disjoint regions. As verified by the simulation results, ELM tends to have better scalability and achieve similar (for regression and binary class cases) or much better (for multiclass cases) generalization performance at much faster learning speed (up to thousands times) than traditional SVM and LS-SVM. [ABSTRACT FROM AUTHOR]

In image processing, cartoon character classification, retrieval, and synthesis are critical, so that cartoonists can effectively and efficiently make cartoons by reusing existing cartoon data. To successfully achieve these tasks, it is essential to extract visual features that comprehensively represent cartoon characters and to construct an accurate distance metric to precisely measure the dissimilarities between cartoon characters. In this paper, we introduce three visual features, color histogram, shape context, and skeleton, to characterize the color, shape, and action, respectively, of a cartoon character. These three features are complementary to each other, and each feature set is regarded as a single view. However, it is improper to concatenate these three features into a long vector, because they have different physical properties, and simply concatenating them into a high-dimensional feature vector will suffer from the so-called curse of dimensionality. Hence, we propose a semisupervised multiview distance metric learning (SSM-DML). SSM-DML learns the multiview distance metrics from multiple feature sets and from the labels of unlabeled cartoon characters simultaneously, under the umbrella of graph-based semisupervised learning. SSM-DML discovers complementary characteristics of different feature sets through an alternating optimization-based iterative algorithm. Therefore, SSM-DML can simultaneously accomplish cartoon character classification and dissimilarity measurement. On the basis of SSM-DML, we develop a novel system that composes the modules of multiview cartoon character classification, multiview graph-based cartoon synthesis, and multiview retrieval-based cartoon synthesis. Experimental evaluations based on the three modules suggest the effectiveness of SSM-DML in cartoon applications. [ABSTRACT FROM AUTHOR]

For the large amount of spatial and spectral information contained in hyperspectral image (HSI), feature description of HSI has attracted widespread concern in recent years. Existing deep learning-based HSI feature description algorithms require a large number of training samples and have poor interpretability. Therefore, it is necessary to develop an efficient HSI features description algorithm with interpretability based on machine learning. Local binary pattern (LBP) is a classical descriptor used to extract the local spatial texture features of images, which has been widely applied to image feature description and matching. However, the existing LBP algorithms for HSI are based on the single-dimensional description, which leads to the limitations on the expression of spatial–spectral information. Therefore, a multidimensional LBP (MDLBP) based on Clifford algebra for HSI is proposed in this article, which is able to extract spatial–spectral feature from multiple dimensions. First, with the theory of the Clifford algebra, a new representation of HSI including spatial and spectral information is built. Second, the geometric relationship between the local geometry of HSI in Clifford algebra space is calculated to realize the local multidimensional description of the local spatial–spectral information. Finally, a novel LBP coding algorithm for HSI is implemented based on the local multidimensional description to calculate the feature descriptor of HSI. The experimental results on HSI classification show that our proposed MDLBP algorithm can achieve higher accuracy than the representative spatial–spectral features and the existing LBP algorithms, especially in the scenery of small-scale training samples. [ABSTRACT FROM AUTHOR]

Aggressive driving is one of the main causes of fatal crashes. Correctly identifying aggressive driving events still represents a challenge in the literature. Furthermore, datasets available for testing the proposed approaches have some limitations since they generally (a) include only a few types of events, (b) contain data collected with only one device, and (c) are generated in drives that did not fully consider the variety of road characteristics and/or driving conditions. The main objective of this work is to compare the performance of several state-of-the-art algorithms for aggressive driving event detection (belonging to anomaly detection-, threshold- and machine learning-based categories) on multiple datasets containing sensors data collected with different devices (black-boxes and smartphones), on different vehicles and in different locations. A secondary objective is to verify whether smartphones could replace black-boxes in aggressive/non-aggressive classification tasks. To this aim, we propose the AD $^2$ (Aggressive Driving Detection) dataset, which contains (i) data collected using multiple devices to evaluate their influence on the algorithm performance, (ii) geographical data useful to analyze the context in which the events occurred, (iii) events recorded in different situations, and (iv) events generated by traveling the same path with aggressive and non-aggressive driving styles, in order to possibly separate the effects of driving style from those of road characteristics. Our experimental results highlighted the superiority of machine learning-based approaches and underlined the ability of smartphones to ensure a level of performance similar to that of black-boxes. [ABSTRACT FROM AUTHOR]

Multilabel learning is now receiving an increasing attention from a variety of domains and many learning algorithms have been witnessed. Similarly, the multilabel learning may also suffer from the problems of high dimensionality, and little attention has been paid to this issue. In this paper, we propose a new ensemble learning algorithms for multilabel data. The main characteristic of our method is that it exploits the features with local discriminative capabilities for each label to serve the purpose of classification. Specifically, for each label, the discriminative capabilities of features on positive and negative data are estimated, and then the top features with the highest capabilities are obtained. Finally, a binary classifier for each label is constructed on the top features. Experimental results on the benchmark data sets show that the proposed method outperforms four popular and previously published multilabel learning algorithms. [ABSTRACT FROM AUTHOR]

Abstract: This paper aims at designing better performing feature-projection based classification algorithms and presents two new such algorithms. These algorithms are batch supervised learning algorithms and represent induced classification knowledge as feature intervals. In both algorithms, each feature participates in the classification by giving real-valued votes to classes. The prediction for an unseen example is the class receiving the highest vote. The first algorithm, OFP.MC, learns on each feature pairwise disjoint intervals which minimize feature classification error. The second algorithm, GFP.MC, constructs feature intervals by greedily improving the feature classification error. The new algorithms are empirically evaluated on twenty datasets from the UCI repository and are compared with the existing feature-projection based classification algorithms (FIL.IF, VFI5, CFP, k-NNFP, and NBC). The experiments demonstrate that the OFP.MC algorithm outperforms other feature-projection based classification algorithms. The GFP.MC algorithm is slightly inferior to the OFP.MC algorithm, but, if it is used for datasets with large number of instances, then it reduces the space requirement of the OFP.MC algorithm. The new algorithms are insensitive to boundary noise unlike the other feature-projection based classification algorithms considered here. [Copyright &y& Elsevier]

Abstract: In order to clarify the role of AdaBoost algorithm for feature selection, classifier learning and its relation with SVM, this paper provided a brief introduction to the AdaBoost which is used for producing a strong classifier out of weak learners firstly. The original adaptive boosting algorithm and its application in face detection and facial expression recognition are reviewed. In pattern classification domain, support vector machine has been widely used and shows promising performance. However, it is expensive in terms of time-consuming. A sort of cascaded support vector machines architecture is capable of improving the classification accuracy based on AdaBoost boosting algorithm, namely, AdaboostSVM. It applied boosting algorithm to feature selection and classifier learning for support vector machine classification and it has achieved approved performance through some researcher''s pioneering work. [Copyright &y& Elsevier]

Abstract: This paper presents a procedure to add broader diversity at the beginning of the evolutionary process. It consists of creating two initial populations with different parameter settings, evolving them for a small number of generations, selecting the best individuals from each population in the same proportion and combining them to constitute a new initial population. At this point the main loop of an evolutionary algorithm is applied to the new population. The results show that our proposal considerably improves both the efficiency of previous methodologies and also, significantly, their efficacy in most of the data sets. We have carried out our experimentation on twelve data sets from the UCI repository and two complex real-world problems which differ in their number of instances, features and classes. [Copyright &y& Elsevier]

Abstract: The inclusion of irrelevant, redundant, and inconsistent features in the data-mining model results in poor predictions and high computational overhead. This paper proposes a novel information theoretic-based interact (IT-IN) algorithm, which concerns the relevance, redundancy, and consistency of the features. The proposed IT-IN algorithm is compared with existing Interact, FCBF, Relief and CFS feature selection algorithms. To evaluate the classification accuracy of IT-IN and remaining four feature selection algorithms, Naïve Bayes, SVM, and ELM classifier are used for ten UCI repository datasets. The proposed IT-IN performs better than existing above algorithms in terms of number of features. The specially designed hash function is used to speed up the IT-IN algorithms and provides minimum computation time than the Interact algorithms. The result clearly reveals that the proposed feature selection algorithm improves the classification accuracy for ELM, Naïve Bayes, and SVM classifiers. The performance of proposed IT-IN with ELM classifier is superior to other classifiers. [ABSTRACT FROM AUTHOR]

Abstract: There are irrelevant features that are redundant or significantly degrade the learning accuracy in the real-world complex classification tasks. This paper presents a new hybrid learning algorithm based on a cooperative coevolutionary algorithm (Co-CEA) with dual populations for designing the radial basis function neural network (RBFNN) models with an explicit feature selection. This approach attempts to complete both the RBFNN construction and the feature selection simultaneously. The proposed algorithm utilizes the Co-CEA’s divide-and-cooperative mechanism, which utilizes the evolutionary algorithms executing in parallel to coevolve subpopulations, corresponding to the hidden layer structure and the dominate features respectively. The algorithm adopts the binary encoding to represent the feature subset and the matrix-form mixed encoding to represent the RBFNN hidden layer structure, and a complete solution is formed via collaborations among the two subpopulations. Experimental results illustrate that the proposed algorithm outperforms other algorithms in references in terms of the classification accuracy, and it is able to obtain both prominent features and good RBFNN structure with higher prediction capability. [Copyright &y& Elsevier]

A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study aims to compare seven manifold learning algorithms: Isomap, Laplacian Eigenmaps, Locally Linear Embedding (LLE), modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to find the best classification accuracy in a multifrequency large-amplitude pulse voltammetry electronic tongue. A sensitivity study of the parameters of each manifold learning algorithm is also included. A data set of seven different aqueous matrices is used to validate the proposed data processing methodology. A leave-one-out cross validation was employed in 63 samples. The best accuracy ( 96.83 % ) was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the t-SNE algorithm for feature extraction, and k-nearest neighbors (kNN) as classifier. [ABSTRACT FROM AUTHOR]