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• W2119388857 abstract "'Kernel' principal component analysis (PCA) is an elegant nonlinear generalisation of the popular linear data analysis method, where a kernel function implicitly defines a nonlinear transformation into a feature space wherein standard PCA is performed. Unfortunately, the technique is not 'sparse', since the components thus obtained are expressed in terms of kernels associated with every training vector. This paper shows that by approximating the covariance matrix in feature space by a reduced number of example vectors, using a maximum-likelihood approach, we may obtain a highly sparse form of kernel PCA without loss of effectiveness." @default.
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• W2119388857 date "2000-01-01" @default.
• W2119388857 modified "2023-09-29" @default.
• W2119388857 title "Sparse Kernel Principal Component Analysis" @default.
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