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Conference Paper

Kernel principal component analysis

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Schölkopf, B., Smola, A., & Müller, K.-R. (1997). Kernel principal component analysis. 7th International Conference on Artificial Neural Networks, ICANN 97, Lausanne, Switzerland, 583-588.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E9CE-F
Abstract
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 highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d-pixel products in images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.