Locality Constraint Dictionary Learning With Support Vector for Pattern Classification. Yin, H., Wu, X., & Chen, S. IEEE Access, 7:175071–175082, 2019. Conference Name: IEEE Accessdoi abstract bibtex Discriminative dictionary learning (DDL) has recently gained significant attention due to its impressive performance in various pattern classification tasks. However, the locality of atoms is not fully explored in conventional DDL approaches which hampers their classification performance. In this paper, we propose a locality constraint dictionary learning with support vector discriminative term (LCDL-SV), in which the locality information is preserved by employing the graph Laplacian matrix of the learned dictionary. To jointly learn a classifier during the training phase, a support vector discriminative term is incorporated into the proposed objective function. Moreover, in the classification stage, the identity of test data is jointly determined by the regularized residual and the learned multi-class support vector machine. Finally, the resulting optimization problem is solved by utilizing the alternative strategy. Experimental results on benchmark databases demonstrate the superiority of our proposed method over previous dictionary learning approaches on both hand-crafted and deep features. The source code of our proposed LCDL-SV is accessible at https://github.com/yinhefeng/LCDL-SV.
@article{yin_locality_2019,
title = {Locality {Constraint} {Dictionary} {Learning} {With} {Support} {Vector} for {Pattern} {Classification}},
volume = {7},
issn = {2169-3536},
doi = {10.1109/ACCESS.2019.2957417},
abstract = {Discriminative dictionary learning (DDL) has recently gained significant attention due to its impressive performance in various pattern classification tasks. However, the locality of atoms is not fully explored in conventional DDL approaches which hampers their classification performance. In this paper, we propose a locality constraint dictionary learning with support vector discriminative term (LCDL-SV), in which the locality information is preserved by employing the graph Laplacian matrix of the learned dictionary. To jointly learn a classifier during the training phase, a support vector discriminative term is incorporated into the proposed objective function. Moreover, in the classification stage, the identity of test data is jointly determined by the regularized residual and the learned multi-class support vector machine. Finally, the resulting optimization problem is solved by utilizing the alternative strategy. Experimental results on benchmark databases demonstrate the superiority of our proposed method over previous dictionary learning approaches on both hand-crafted and deep features. The source code of our proposed LCDL-SV is accessible at https://github.com/yinhefeng/LCDL-SV.},
language = {en},
journal = {IEEE Access},
author = {Yin, He-Feng and Wu, Xiao-Jun and Chen, Su-Gen},
year = {2019},
note = {Conference Name: IEEE Access},
keywords = {\#Classification, \#Locality, /unread, Dictionaries, Dictionary learning, Image coding, Laplace equations, Machine learning, Optimization, Support vector machines, Training, locality constraint, pattern classification, support vector discriminative term},
pages = {175071--175082},
}
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To jointly learn a classifier during the training phase, a support vector discriminative term is incorporated into the proposed objective function. Moreover, in the classification stage, the identity of test data is jointly determined by the regularized residual and the learned multi-class support vector machine. Finally, the resulting optimization problem is solved by utilizing the alternative strategy. Experimental results on benchmark databases demonstrate the superiority of our proposed method over previous dictionary learning approaches on both hand-crafted and deep features. 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