Nonlinear Component Analysis as a Kernel Eigenvalue Problem¶

Schölkopf, B., Smola, A., & Müller, K. (1998). Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation, 10(5), 1299-1319.

Type: Journal article
Intellectual base: Primary research
Year: 1998
Volume: 10
Issue: 5
Pages: 1299-1319
DOI: 10.1162/089976698300017467
DOI registrant: MIT Press
Link: https://doi.org/10.1162/089976698300017467
Cited from: statistics_experimental_design

Cited by¶

3 citations across 2 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes¶

Dimensionality Reduction
- Listed in the references but not attached to a specific claim.

Domain-specific¶

Kernel principal component analysis
- Kernel PCA centers a positive-semidefinite Gram matrix and eigendecomposes it so principal axes correspond to variance directions in the kernel-induced feature space.

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Registry ID ref:b9eacda7fc58 · see in the full table