Principal manifolds for data visualization and dimension reduction /
Principal manifolds for data visualization and dimension reduction /
Alexander N. Gorban,Balázs Kégl, Donald C. Wunsch, Andrei Zinovyev, editors.
- Berlin ; New York : Springer, 2008.
- xxiii, 334 pages : illustration (some columns) ; 24 cm.
- Lecture notes in computational science and engineering 58 .
"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.
9783540737490 3540737499
2007932175
Principal components analysis.
Statistics--Graphic methods.
Mathematical statistics
Análisis de componentes principales
Estadística--Métodos gráficos
Estadística matemática
001.4226028566
"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.
9783540737490 3540737499
2007932175
Principal components analysis.
Statistics--Graphic methods.
Mathematical statistics
Análisis de componentes principales
Estadística--Métodos gráficos
Estadística matemática
001.4226028566