Abstract
Sparse representation classification (SRC) plays an important role in pattern recognition. Recently, a more generic method named as collaborative representation classification (CRC) has greatly improved the efficiency of SRC. By taking advantage of recent development of CRC, this paper explores to smoothly apply the kernel technique to further improve its performance and proposes the kernel CRC (KCRC) approach. Tested by multiple databases in experiments, KCR-C has shown that it can perfectly classify the data with the same direction distribution with limited complexity, and outperforms CRC, SRC and some other conventional algorithms.
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