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Kernel principal component analysis

1997

Conference Paper

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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 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.

Author(s): Schölkopf, B. and Smola, AJ. and Müller, K-R.
Book Title: Artificial neural networks: ICANN ’97, LNCS, vol. 1327
Journal: 7th International Conference on Artificial Neural Networks, ICANN 97, Lausanne, Switzerland
Pages: 583-588
Year: 1997
Month: October
Day: 0
Editors: W Gerstner and A Germond and M Hasler and J-D Nicoud
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1007/BFb0020217
Event Name: 7th International Conference on Artificial Neural Networks
Event Place: Lausanne, Switzerland

Address: Berlin, Germany
Digital: 0
ISBN: 3-540-63631-5
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@inproceedings{421,
  title = {Kernel principal component analysis},
  author = {Sch{\"o}lkopf, B. and Smola, AJ. and M{\"u}ller, K-R.},
  journal = {7th International Conference on Artificial Neural Networks, ICANN 97, Lausanne, Switzerland},
  booktitle = {Artificial neural networks: ICANN '97, LNCS, vol. 1327},
  pages = {583-588},
  editors = {W Gerstner and A Germond and M Hasler and J-D Nicoud},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = oct,
  year = {1997},
  month_numeric = {10}
}