Locality Constraint Dictionary Learning with Support Vector for Pattern Classification

TL;DR

This paper introduces LCDL-SV, a novel discriminative dictionary learning method that incorporates locality constraints and support vector discriminative terms to improve pattern classification accuracy.

Contribution

It proposes a new approach combining locality-preserving dictionary learning with support vector discriminative terms and joint classifier training.

Findings

Outperforms previous dictionary learning methods on benchmark datasets.

Effective in both hand-crafted and deep feature-based classification tasks.

Demonstrates the importance of locality constraints in dictionary learning.

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