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B. Sch\"olkopf, A. Smola, and K.-R. M\"uller (1998)

Nonlinear component analysis as a kernel Eigenvalue problem

Neural Computation, 10:1299–1319.

Abstract

Applies the kernel method to unsupervised algorithms as for instance Principal Component Analysis. This gives a principled and efficient approach to nonlinear PCA.

Note

Technical Report No. 44, 1996, Max Planck Institut für biologische Kybernetik, Tübingen

URL /papers/nlpca.ps.gz

by admin — last modified 2007-01-31 11:07