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• W2040848081 abstract "We propose Kernel Self-optimized Locality Preserving Discriminant Analysis (KSLPDA) for feature extraction and recognition. The procedure of KSLPDA is divided into two stages, i.e., one is to solve the optimal expansion of the data-dependent kernel with the proposed kernel self-optimization method, and the second is to seek the optimal projection matrix for dimensionality reduction. Since the optimal parameters of data-dependent kernel are achieved automatically through solving the constraint optimization equation, based on maximum margin criterion and Fisher criterion in the empirical feature space, KSLPDA works well on feature extraction for classification. The comparative experiments show that KSLPDA outperforms PCA, LDA, LPP, supervised LPP and kernel supervised LPP." @default.
• W2040848081 created "2016-06-24" @default.
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• W2040848081 creator A5050960882 @default.
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• W2040848081 date "2011-10-01" @default.
• W2040848081 modified "2023-09-26" @default.
• W2040848081 title "Kernel Self-optimized Locality Preserving Discriminant Analysis for feature extraction and recognition" @default.
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• W2040848081 doi "https://doi.org/10.1016/j.neucom.2011.04.017" @default.
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