  During the past decade, representation-based classification methods have
received considerable attention in pattern recognition. In particular, the
recently proposed non-negative representation based classification (NRC) method
has been reported to achieve promising results in a wide range of
classification tasks. However, NRC has two major drawbacks. First, there is no
regularization term in the formulation of NRC, which may result in unstable
solution and misclassification. Second, NRC ignores the fact that data usually
lies in a union of multiple affine subspaces, rather than linear subspaces in
practical applications. To address the above issues, this paper presents an
affine non-negative collaborative representation (ANCR) model for pattern
classification. To be more specific, ANCR imposes a regularization term on the
coding vector. Moreover, ANCR introduces an affine constraint to better
represent the data from affine subspaces. The experimental results on several
benchmarking datasets demonstrate the merits of the proposed ANCR method. The
source code of our ANCR is publicly available at
https://github.com/yinhefeng/ANCR.
