1 research outputs found
Sparse, Collaborative, or Nonnegative Representation: Which Helps Pattern Classification?
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 -norm sparsity or
the -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