Non-negative Sparse and Collaborative Representation for Pattern Classification

TL;DR

This paper introduces a Non-negative Sparse and Collaborative Representation (NSCR) method that enhances pattern classification by making sparse and collaborative representations more discriminative through non-negativity constraints, outperforming existing approaches.

Contribution

The paper proposes a novel NSCR method that incorporates non-negativity into sparse and collaborative representations, improving classification accuracy over previous methods.

Findings

NSCR outperforms previous SR and CR methods on benchmark datasets.

NSCR surpasses state-of-the-art deep learning approaches in various pattern classification tasks.

Non-negativity enhances the discriminative power of sparse and collaborative representations.

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.