New sparse subspace learning approaches for feature abstraction and recognition

Abstract

In this paper, we propose two novel sparse representation based dimension reduction approaches for feature abstraction and recognition: sparse local preserving projection (SLPP) and structural sparse local preserving projection (SSLPP). They are efficient in detecting the nonlinear features of the intrinsic manifold structure, also improving the… (More)

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