Kernel principal component analysis
1997
Conference Paper
ei
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}, doi = {10.1007/BFb0020217}, month_numeric = {10} } |