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Principal Manifolds for Data Visualization and Dimension Reduction: 58 (Lecture Notes in Computational Science and Engineering) - Softcover
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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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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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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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Zustand: Fine. *Price HAS BEEN temporarily REDUCED by 10% until Monday, Sept. 8 (sale item)* 340 pp., paperback, fine. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country. Artikel-Nr. ZB1327714
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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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Taschenbuch. Zustand: Neu. 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-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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 364 pp. Englisch. Artikel-Nr. 9783540737490
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Principal Manifolds for Data Visualization and Dimension Reduction
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Artikel-Nr. 9783540737490
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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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Paperback. Zustand: Brand New. 1st edition. 334 pages. 9.00x6.00x0.50 inches. In Stock. Artikel-Nr. x-3540737499
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