  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.
