Principal manifolds for data visualization and dimension reduction / Alexander N. Gorban,Balázs Kégl, Donald C. Wunsch, Andrei Zinovyev, editors.
Material type: TextSeries: Lecture notes in computational science and engineering ; 58Publication details: Berlin ; New York : Springer, 2008.Description: xxiii, 334 pages : illustration (some columns) ; 24 cmISBN:- 9783540737490
- 3540737499
- 001.4226028566
Item type | Current library | Call number | Copy number | Status | Date due | Barcode | Item holds | |
---|---|---|---|---|---|---|---|---|
Colección general | Biblioteca Yachay Tech | 001.4226028566 P9573 2008 (Browse shelf(Opens below)) | Ej. 1 | Available | 001407 | |||
Colección general | Biblioteca Yachay Tech | 001.4226028566 P9573 2008 (Browse shelf(Opens below)) | Ej. 2 | Available | 001408 | |||
Colección general | Biblioteca Yachay Tech | 001.4226028566 P9573 2008 (Browse shelf(Opens below)) | Ej. 3 | Available | 001409 |
"This book is a collection of reviews and original papers presented partially at the workshop 'Principal manifolds for data cartography and dimension reduction' (Leicester, August 24-26, 2006)."--P. X.
Includes bibliographical references and index.
Contents: Developments and applications of nolinear principal componet analysis - a review -- Nonlinear principal component analysis: neutral network models and applications -- Learning nonlinear principal manifolds by self-organising maps -- Elastic maps and nets for approximating principal manifolds and their application to microarray data visualization -- Topology-preserving mappong for data visualisation -- The iterative extraction approach to clustering -- Representating complex data using localized principal components with application to astronomical data -- Auto - associative models, nonlinear principal component analysis, manifolds and projection pursuit -- Beyond the concept of manifolds: principal tres, metro maps, and elastic cubic complexes -- Diffusion maps - a probabilistic interpretation fpor spectral embedding and clustering algorithms -- On bounds for diffusion, discrepancy and fill distance metrics -- Gemetric optimization methods for the analysis of gene expression data -- Dimensionality reduction and microarray data -- PCA and K.Means decipher genome.
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