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This paper presents a novel algorithm for the dimensionality reduction which employs compressed sensing (CS) to improve the generalization capability of a classifier, especially for large-scale data. Compared to traditional dimensionality reduction methods, the proposed algorithm makes no use of the problem-dependent parameters, nor does it require additional computation for the eigenvalue decomposition...
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