Producing computationally efficient KPCA-based feature extraction for classification problems

Producing computationally efficient KPCA-based feature extraction for classification problems

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An improvement to kernel principal component analysis (KPCA) to produce computationally efficient KPCA-based feature extraction is proposed. This improvement is applicable to all cases no matter whether the samples in the feature space have zero mean or not. Experiments on several benchmark datasets show that the improvement performs well in classification problems.


    1. 1)
      • Y. Xu , D. Zhang , F. Song , J.-Y. Yang , Z. Jing , M. Li . A method for speeding up feature extraction based on KPCA. Neurocomputing , 1056 - 1061
    2. 2)
      • J. Yang , Z. Jin , J.-Y. Yang , D. Zhang . Essence of kernel Fisher discriminant: KPCA plus LDA. Pattern Recognit. , 2097 - 2100
    3. 3)
      • Y. Xu , J.-Y. Yang , J. Lu , D.-J. Yu . An efficient renovation on kernel Fisher discriminant analysis and face recognition experiments. Pattern Recognit. , 2091 - 2094
    4. 4)
      • Y. Xu , J.-Y. Yang , J. Yang . A reformative kernel Fisher discriminant analysis. Pattern Recognit. , 1299 - 1302
    5. 5)
      • Twining, C., Taylor, C.: `Kernel principal component analysis and the construction of non-linear active shape models', British Machine Vision Conf., 2001, Manchester, UK, 1, p. 23–32.

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