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- W2166093990 abstract "Kernel principal component analysis (KPCA) could extract nonlinear features from samples, however, its feature extraction efficiency is inversely proportional to the size of the training sample set. This paper proposes an efficient KPCA method that is much faster than the KPCA in extracting features from samples. The proposed method first selects nodes from the training samples, then formulates the novel feature extraction scheme. Experimental results illustrate that the proposed method is effective." @default.
- W2166093990 created "2016-06-24" @default.
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- W2166093990 date "2009-11-01" @default.
- W2166093990 modified "2023-09-22" @default.
- W2166093990 title "A fast KPCA-based nonlinear feature extraction method" @default.
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- W2166093990 doi "https://doi.org/10.1109/paciia.2009.5406645" @default.
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