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Nonlinear Component Analysis as a Kernel Eigenvalue Problem
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 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.
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Metadata
- Authors
- Schölkopf, B., Smola, A., & Müller, K.-R.
- Venue
- Neural Computation
- Publisher
- MIT Press - Journals
- Published
- 1998-07-01
- Volume
- 10
- Issue
- 5
- Pages
- 1299-1319
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2140095548
- Cited by
- 8,084
- References
- 4
- FWCI
- 43.32
- Type
- journal article
- Language
- en
- Open access
- closed
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