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Neural Computation In Neural Computation, Vol. 10, No. 5. (1 July 1998), pp. 1299-1319, doi:10.1162/089976698300017467 Key: citeulike:3474398
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A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map?for instance, the space of all possible five-pixel products in 16 ? 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition. A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map?for instance, the space of all possible five-pixel products in 16 ? 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.
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