Sparse, Collaborative, or Nonnegative Representation: Which Helps Pattern Classification?

TL;DR

This paper compares sparse, collaborative, and nonnegative representations for pattern classification, showing that nonnegative representation enhances discriminative power and outperforms existing methods, especially with deep features.

Contribution

It introduces nonnegative representation (NR) for pattern classification, demonstrating its advantages over sparse and collaborative methods in boosting discriminative power.

Findings

NR improves representation of homogeneous samples

NR limits representation of heterogeneous samples

NRC achieves state-of-the-art performance with deep features

Abstract

The use of sparse representation (SR) and collaborative representation (CR) for pattern classification has been widely studied in tasks such as face recognition and object categorization. Despite the success of SR/CR based classifiers, it is still arguable whether it is the $\ell_{1}$-norm sparsity or the $\ell_{2}$-norm collaborative property that brings the success of SR/CR based classification. In this paper, we investigate the use of nonnegative representation (NR) for pattern classification, which is largely ignored by previous work. Our analyses reveal that NR can boost the representation power of homogeneous samples while limiting the representation power of heterogeneous samples, making the representation sparse and discriminative simultaneously and thus providing a more effective solution to representation based classification than SR/CR. Our experiments demonstrate that the proposed NR based classifier (NRC) outperforms previous representation based classifiers. With deep features as inputs, it also achieves state-of-the-art performance on various visual classification tasks.