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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 232612.0, MPI für biologische Kybernetik / Biologische Kybernetik
A kernel view of the dimensionality reduction of manifolds
Authors:Ham, J.; Lee, D.D.; Mika, S.; Schölkopf, B.
Editors:Greiner, R.; Schuurmans, D.
Date of Publication (YYYY-MM-DD):2004
Title of Proceedings:Proceedings of the Twenty-First International Conference on Machine Learning
Start Page:369
End Page:376
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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.
Comment of the Author/Creator:also appeared as MPI-TR 110
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:2326
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