Kernel PCA and De-noising in feature spaces
1999
Conference Paper
ei
Kernel PCA as a nonlinear feature extractor has proven powerful as a preprocessing step for classification algorithms. But it can also be considered as a natural generalization of linear principal component analysis. This gives rise to the question how to use nonlinear features for data compression, reconstruction, and de-noising, applications common in linear PCA. This is a nontrivial task, as the results provided by kernel PCA live in some high dimensional feature space and need not have pre-images in input space. This work presents ideas for finding approximate pre-images, focusing on Gaussian kernels, and shows experimental results using these pre-images in data reconstruction and de-noising on toy examples as well as on real world data.
Author(s): | Mika, S. and Schölkopf, B. and Smola, AJ. and Müller, K-R. and Scholz, M. and Rätsch, G. |
Book Title: | Advances in Neural Information Processing Systems 11 |
Journal: | Advances in Neural Information Processing Systems |
Pages: | 536-542 |
Year: | 1999 |
Month: | June |
Day: | 0 |
Editors: | MS Kearns and SA Solla and DA Cohn |
Publisher: | MIT Press |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | 12th Annual Conference on Neural Information Processing Systems (NIPS 1998) |
Event Place: | Denver, CO, USA |
Address: | Cambridge, MA, USA |
Digital: | 0 |
ISBN: | 0-262-11245-0 |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
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BibTex @inproceedings{806, title = {Kernel PCA and De-noising in feature spaces}, author = {Mika, S. and Sch{\"o}lkopf, B. and Smola, AJ. and M{\"u}ller, K-R. and Scholz, M. and R{\"a}tsch, G.}, journal = {Advances in Neural Information Processing Systems}, booktitle = {Advances in Neural Information Processing Systems 11}, pages = {536-542 }, editors = {MS Kearns and SA Solla and DA Cohn}, publisher = {MIT Press}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Cambridge, MA, USA}, month = jun, year = {1999}, doi = {}, month_numeric = {6} } |