Sparse, Collaborative, or Nonnegative Representation: Which Helps Pattern Classification?
This work addresses a fundamental debate in representation-based classification for computer vision, offering a novel approach that improves accuracy in tasks like face recognition and object categorization.
The paper tackles the problem of determining whether sparsity or collaboration drives the success of representation-based classifiers in pattern classification, and finds that nonnegative representation (NR) boosts representation power for homogeneous samples while limiting it for heterogeneous ones, leading to state-of-the-art performance on visual classification tasks with deep features.
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.