In 1901, Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a prototype for many other tools of data analysis, visualization and dimension reduction: Independent Component Analysis (ICA), Multidimensional Scaling (MDS), Nonlinear PCA (NLPCA), Self Organizing Maps (SOM), etc. The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described as well. Presentation of algorithms is supplemented by case studies, from engineering to astronomy, but mostly of biological data: analysis of microarray and metabolite data. The volume ends with a tutorial ‘PCA and K-means decipher genome’. The book is meant to be useful for practitioners in applied data analysis in life sciences, engineering, physics and chemistry; it will also be valuable to Ph D students and researchers in computer sciences, applied mathematics and statistics.
Alexander N. Gorban & Balázs Kégl
Principal Manifolds for Data Visualization and Dimension Reduction [PDF ebook]
Principal Manifolds for Data Visualization and Dimension Reduction [PDF ebook]
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Idioma Inglés ● Formato PDF ● Páginas 340 ● ISBN 9783540737506 ● Tamaño de archivo 8.1 MB ● Editor Alexander N. Gorban & Balázs Kégl ● Editorial Springer Berlin ● Ciudad Heidelberg ● País DE ● Publicado 2007 ● Descargable 24 meses ● Divisa EUR ● ID 2163547 ● Protección de copia DRM social