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- W1569191925 abstract "We present a locality preserving K-SVD (LP-KSVD) algorithm for joint dictionary and classifier learning, and further incorporate kernel into our framework. In LP-KSVD, we construct a locality preserving term based on the relations between input samples and dictionary atoms, and introduce the locality via nearest neighborhood to enforce the locality of representation. Motivated by the fact that locality-related methods works better in a more discriminative and separable space, we map the original feature space to the kernel space, where samples of different classes become more separable. Experimental results show the proposed approach has strong discrimination power and is comparable or outperforms some state-of-the-art approaches on public databases." @default.
- W1569191925 created "2016-06-24" @default.
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- W1569191925 date "2015-06-01" @default.
- W1569191925 modified "2023-09-24" @default.
- W1569191925 title "Joint kernel dictionary and classifier learning for sparse coding via locality preserving K-SVD" @default.
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- W1569191925 doi "https://doi.org/10.1109/icme.2015.7177438" @default.
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