Téléchargez le livre : Principal Manifolds for Data Visualization and Dimension Reduction
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Description
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 PhD students and researchers in computer sciences, applied mathematics and statistics.
Pages
340 pages
Collection
Lecture Notes in Computational Science and Engineering
Parution
2007-09-11
Marque
Springer
EAN papier
9783540737490
EAN PDF
9783540737506
Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
34
Taille du fichier
13496 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
231,04 €

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