@inproceedings{b27bf5f75106418e930d25243d4eb70c,
title = "Kernel principal component analysis",
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 high-dimensional 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.",
author = "Bernhard Sch{\"o}lkopf and Alexander Smola and M{\"u}ller, {Klaus Robert}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1997.; 7th International Conference on Artificial Neural Networks, ICANN 1997 ; Conference date: 08-10-1997 Through 10-10-1997",
year = "1997",
doi = "10.1007/bfb0020217",
language = "English",
isbn = "3540636315",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "583--588",
editor = "Wulfram Gerstner and Alain Germond and Martin Hasler and Jean-Daniel Nicoud",
booktitle = "Artificial Neural Networks - ICANN 1997 - 7th International Conference, Proceeedings",
}