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.
Original language | English |
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Title of host publication | Artificial Neural Networks - ICANN 1997 - 7th International Conference, Proceeedings |
Editors | Wulfram Gerstner, Alain Germond, Martin Hasler, Jean-Daniel Nicoud |
Publisher | Springer Verlag |
Pages | 583-588 |
Number of pages | 6 |
ISBN (Print) | 3540636315, 9783540636311 |
DOIs | |
Publication status | Published - 1997 |
Event | 7th International Conference on Artificial Neural Networks, ICANN 1997 - Lausanne, Switzerland Duration: 1997 Oct 8 → 1997 Oct 10 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 1327 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Other
Other | 7th International Conference on Artificial Neural Networks, ICANN 1997 |
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Country/Territory | Switzerland |
City | Lausanne |
Period | 97/10/8 → 97/10/10 |
Bibliographical note
Publisher Copyright:© Springer-Verlag Berlin Heidelberg 1997.
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science