PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Kernel view of the dimensionality reduction of manifolds
J Ham, D Lee, Sebastian Mika and Bernhard Schölkopf
In: Proceedings ICML 04(2004).

Abstract

We interpret several well-known algorithms for dimensionality reduction of manifolds as kernel methods. Isomap, graph Laplacian eigenmap, and locally linear embedding (LLE) all utilize local neighborhood information to construct a global embedding of the manifold. We show how all three algorithms can be described as kernel PCA on specially constructed Gram matrices, and illustrate the similarities and differences between the algorithms with representative examples.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:835
Deposited By:Sebastian Mika
Deposited On:01 January 2005