Geometric structure of high-dimensional data and dimensionality reduction
Pt. 1. Data geometrypt. 2. Linear dimensionality reduction -- pt. 3. Nonlinear dimensionality reduction.
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| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Beijing
Higher Education Press
2012
Berlin Springer 2012 Heidelberg u.a. |
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| Online Zugang: | Inhaltstext Cover Inhaltsverzeichnis Inhaltstext |
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| Zusammenfassung: | Pt. 1. Data geometrypt. 2. Linear dimensionality reduction -- pt. 3. Nonlinear dimensionality reduction. Introduction Part I. Data geometry. Preliminary calculus on manifolds -- Geometric structure of high-dimensional data -- Data models and structures of kernels of DR -- Part II. Linear dimensionality reduction. Principal component analysis -- Classical multidimensional scaling -- Random projection -- Part III. Nonlinear dimensionality reduction. Isomaps -- Maximum variance unfolding -- Locally linear embedding -- Local tangent space alignment -- Laplacian Eigenmaps -- Hessian locally linear embedding -- Diffusion maps -- Fast algorithms for DR approximation -- Appendix A. Differential forms and operators on manifolds -- Index. |
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| Beschreibung: | Literaturangaben |
| Beschreibung: | XVIII, 356 S. Ill., graph. Darst. 24 cm |
| ISBN: | 9787040317046 978-7-04-031704-6 364227496X 3-642-27496-X 9783642274961 978-3-642-27496-1 |