Nonlinear Component Analysis as a Kernel Eigenvalue Problem
1998
Article
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 high-dimensional feature spaces, related to input space by some nonlinear map—for instance, the space of all possible five-pixel products in 16 × 16 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. |
Journal: | Neural Computation |
Volume: | 10 |
Number (issue): | 5 |
Pages: | 1299-1319 |
Year: | 1998 |
Month: | July |
Day: | 0 |
Department(s): | Empirische Inferenz |
Bibtex Type: | Article (article) |
Digital: | 0 |
DOI: | 10.1162/089976698300017467 |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
Web
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BibTex @article{730, title = {Nonlinear Component Analysis as a Kernel Eigenvalue Problem}, author = {Sch{\"o}lkopf, B. and Smola, AJ. and M{\"u}ller, K-R.}, journal = {Neural Computation}, volume = {10}, number = {5}, pages = {1299-1319}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, month = jul, year = {1998}, doi = {10.1162/089976698300017467}, month_numeric = {7} } |