  Representation based classification method (RBCM) remains one of the hottest
topics in the community of pattern recognition, and the recently proposed
non-negative representation based classification (NRC) achieved impressive
recognition results in various classification tasks. However, NRC ignores the
relationship between the coding and classification stages. Moreover, there is
no regularization term other than the reconstruction error term in the
formulation of NRC, which may result in unstable solution leading to
misclassification. To overcome these drawbacks of NRC, in this paper, we
propose a class-specific residual constraint non-negative representation (CRNR)
for pattern classification. CRNR introduces a class-specific residual
constraint into the formulation of NRC, which encourages training samples from
different classes to competitively represent the test sample. Based on the
proposed CRNR, we develop a CRNR based classifier (CRNRC) for pattern
classification. Experimental results on several benchmark datasets demonstrate
the superiority of CRNRC over conventional RBCM as well as the recently
proposed NRC. Moreover, CRNRC works better or comparable to some
state-of-the-art deep approaches on diverse challenging pattern classification
tasks. The source code of our proposed CRNRC is accessible at
https://github.com/yinhefeng/CRNRC.
