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Collaborative representation based classifier (CRC) has been successfully applied to pattern classification. However, CRC may not be able to identify the data with highly nonlinear distribution as a linear algorithm. In this paper, we first propose a kernel locality-constrained collaborative representation based classifier (KLCRC). KLCRC is a nonlinear extension of CRC, and it introduces the local structures of data sets into collaborative representation methods. Since the kernel feature space has a very high (or possibly infinite) dimensionality, we present a dimensionality reduction method (termed kernel locality-constrained collaborative representation based discriminant analysis, KLCR-DA) which can fit KLCRC well. KLCR-DA seeks a subspace in which the between-class reconstruction residual of a given data set is maximized and the within-class reconstruction residual is minimized. Hence, KLCRC can achieve …