Non-negative Sparse and Collaborative Representation for Pattern Classification

Jun Xu; Zhou Xu; Wangpeng An; Haoqian Wang; David Zhang

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arXiv · 1908.07956

Non-negative Sparse and Collaborative Representation for Pattern Classification

Jun Xu·Zhou Xu·Wangpeng An·Haoqian Wang·David Zhang

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Abstract

Sparse representation (SR) and collaborative representation (CR) have been successfully applied in many pattern classification tasks such as face recognition. In this paper, we propose a novel Non-negative Sparse and Collaborative Representation (NSCR) for pattern classification. The NSCR representation of each test sample is obtained by seeking a non-negative sparse and collaborative representation vector that represents the test sample as a linear combination of training samples. We observe that the non-negativity can make the SR and CR more discriminative and effective for pattern classification. Based on the proposed NSCR, we propose a NSCR based classifier for pattern classification. Extensive experiments on benchmark datasets demonstrate that the proposed NSCR based classifier outperforms the previous SR or CR based approach, as well as state-of-the-art deep approaches, on diverse challenging pattern classification tasks.

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BibTeX RIS

Jun Xu, Zhou Xu, Wangpeng An, Haoqian Wang, David Zhang. 2019-08-20. Non-negative Sparse and Collaborative Representation for Pattern Classification. https://arxiv.org/abs/1908.07956

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