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Principal Manifolds for Data Visualization and Dimension Reduction (Lecture Notes in Computational Science and Engineering, 58) - Softcover
Synopsis
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. Presentation of algorithms is supplemented by case studies. The volume ends with a tutorial PCA deciphers genome.
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- PublisherSpringer
- Publication date2007
- ISBN 10 3540737499
- ISBN 13 9783540737490
- BindingPaperback
- LanguageEnglish
- Number of pages364
- EditorAlexander N. Gorban, Balázs Kégl, Donald C. Wunsch, Andrei Zinovyev
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Principal Manifolds for Data Visualization and Dimension Reduction
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Principal Manifolds for Data Visualization and Dimension Reduction
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Principal Manifolds for Data Visualization and Dimension Reduction
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Principal Manifolds For Data Visualization And Dimension Reduction
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Principal Manifolds For Data Visualization And Dimension Reduction
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Principal Manifolds for Data Visualization and Dimension Reduction (Lecture Notes in Computational Science and Engineering, 58)
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Principal Manifolds for Data Visualization and Dimension Reduction
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Springer Berlin Heidelberg, 2007
ISBN 10: 3540737499
ISBN 13: 9783540737490
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are describedPresentation of algorithms is supplemented by case studiesThe book starts with the quote of the classical Pea. Seller Inventory # 4899777
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Principal Manifolds for Data Visualization and Dimension Reduction (Lecture Notes in Computational Science and Engineering, 58)
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Principal Manifolds for Data Visualization and Dimension Reduction (Lecture Notes in Computational Science and Engineering (58))
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Principal Manifolds for Data Visualization and Dimension Reduction
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Springer Berlin Heidelberg Okt 2007, 2007
ISBN 10: 3540737499
ISBN 13: 9783540737490
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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-meansdecipher 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. 364 pp. Englisch. Seller Inventory # 9783540737490
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