Abstract Kernel principal component analysis(KPCA) can extract nonlinear features of datasets. However,its efficiency is inversely proportional to the size of the training sample set. In this paper,an adaptive kernel feature subspace method is proposed to extract features efficiently. This method is methodologically consistent with KPCA,and it improves the efficiency by adaptively selecting the spanning vectors of the KPCA without losing accuracy. Experimental results on two-dimensional data and MNIST datasets show that the proposed method is better than the one associated with KPCA and reference methods.
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2013, Vol. 26 
