Class-specific residual constraint non-negative representation for pattern classification

TL;DR

This paper introduces CRNR, a novel class-specific residual constraint method for non-negative representation in pattern classification, improving stability and accuracy over existing methods and achieving competitive results with deep learning approaches.

Contribution

The paper proposes CRNR, a new regularization approach that incorporates class-specific residual constraints into NRC, enhancing classification stability and performance.

Findings

CRNRC outperforms conventional RBCM and NRC on benchmark datasets.

CRNRC achieves comparable or better results than some deep learning methods.

The method demonstrates robustness across diverse pattern classification tasks.

Abstract

Representation based classification method (RBCM) remains one of the hottest topics in the community of pattern recognition, and the recently proposed non-negative representation based classification (NRC) achieved impressive recognition results in various classification tasks. However, NRC ignores the relationship between the coding and classification stages. Moreover, there is no regularization term other than the reconstruction error term in the formulation of NRC, which may result in unstable solution leading to misclassification. To overcome these drawbacks of NRC, in this paper, we propose a class-specific residual constraint non-negative representation (CRNR) for pattern classification. CRNR introduces a class-specific residual constraint into the formulation of NRC, which encourages training samples from different classes to competitively represent the test sample. Based on the proposed CRNR, we develop a CRNR based classifier (CRNRC) for pattern classification. Experimental results on several benchmark datasets demonstrate the superiority of CRNRC over conventional RBCM as well as the recently proposed NRC. Moreover, CRNRC works better or comparable to some state-of-the-art deep approaches on diverse challenging pattern classification tasks. The source code of our proposed CRNRC is accessible at https://github.com/yinhefeng/CRNRC.