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Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary: Adopting data geometry as a framework to address dimensionality reduction, this volume introduces well known linear methods, stressing recently developed ones, and covers various dimensionality reduction applications including hyperspectral imagery. Buchnummer des Verkäufers

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"Geometric Structure of High-Dimensional Data and Dimensionality Reduction" adopts data geometry as a framework to address various methods of dimensionality reduction. In addition to the introduction to well-known linear methods, the book moreover stresses the recently developed nonlinear methods and introduces the applications of dimensionality reduction in many areas, such as face recognition, image segmentation, data classification, data visualization, and hyperspectral imagery data analysis. Numerous tables and graphs are included to illustrate the ideas, effects, and shortcomings of the methods. MATLAB code of all dimensionality reduction algorithms is provided to aid the readers with the implementations on computers. 

The book will be useful for mathematicians, statisticians, computer scientists, and data analysts. It is also a valuable handbook for other practitioners who have a basic background in mathematics, statistics and/or computer algorithms, like internet search engine designers, physicists, geologists, electronic engineers, and economists.

Jianzhong Wang is a Professor of Mathematics at Sam Houston State University, U.S.A.

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Buchbeschreibung Springer, 2012. Hardback. Buchzustand: NEW. 9783642274961 Hardback, This listing is a new book, a title currently in-print which we order directly and immediately from the publisher. Buchnummer des Verkäufers HTANDREE0358316

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Buchbeschreibung Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, 2012. HRD. Buchzustand: New. New Book. Shipped from US within 10 to 14 business days. Established seller since 2000. Buchnummer des Verkäufers IB-9783642274961

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Buchbeschreibung Springer-Verlag Gmbh Mrz 2012, 2012. Buch. Buchzustand: Neu. 240x168x28 mm. Neuware - 'Geometric Structure of High-Dimensional Data and Dimensionality Reduction' adopts data geometry as a framework to address various methods of dimensionality reduction. In addition to the introduction to well-known linear methods, the book moreover stresses the recently developed nonlinear methods and introduces the applications of dimensionality reduction in many areas, such as face recognition, image segmentation, data classification, data visualization, and hyperspectral imagery data analysis. Numerous tables and graphs are included to illustrate the ideas, effects, and shortcomings of the methods. MATLAB code of all dimensionality reduction algorithms is provided to aid the readers with the implementations on computers. The book will be useful for mathematicians, statisticians, computer scientists, and data analysts. It is also a valuable handbook for other practitioners who have a basic background in mathematics, statistics and/or computer algorithms, like internet search engine designers, physicists, geologists, electronic engineers, and economists. Jianzhong Wang is a Professor of Mathematics at Sam Houston State University, U.S.A. 356 pp. Englisch. Buchnummer des Verkäufers 9783642274961

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Buchbeschreibung Springer-Verlag Gmbh Mrz 2012, 2012. Buch. Buchzustand: Neu. 240x168x28 mm. Neuware - 'Geometric Structure of High-Dimensional Data and Dimensionality Reduction' adopts data geometry as a framework to address various methods of dimensionality reduction. In addition to the introduction to well-known linear methods, the book moreover stresses the recently developed nonlinear methods and introduces the applications of dimensionality reduction in many areas, such as face recognition, image segmentation, data classification, data visualization, and hyperspectral imagery data analysis. Numerous tables and graphs are included to illustrate the ideas, effects, and shortcomings of the methods. MATLAB code of all dimensionality reduction algorithms is provided to aid the readers with the implementations on computers. The book will be useful for mathematicians, statisticians, computer scientists, and data analysts. It is also a valuable handbook for other practitioners who have a basic background in mathematics, statistics and/or computer algorithms, like internet search engine designers, physicists, geologists, electronic engineers, and economists. Jianzhong Wang is a Professor of Mathematics at Sam Houston State University, U.S.A. 356 pp. Englisch. Buchnummer des Verkäufers 9783642274961

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Buchbeschreibung Springer-Verlag Gmbh Mrz 2012, 2012. Buch. Buchzustand: Neu. 240x168x28 mm. Neuware - 'Geometric Structure of High-Dimensional Data and Dimensionality Reduction' adopts data geometry as a framework to address various methods of dimensionality reduction. In addition to the introduction to well-known linear methods, the book moreover stresses the recently developed nonlinear methods and introduces the applications of dimensionality reduction in many areas, such as face recognition, image segmentation, data classification, data visualization, and hyperspectral imagery data analysis. Numerous tables and graphs are included to illustrate the ideas, effects, and shortcomings of the methods. MATLAB code of all dimensionality reduction algorithms is provided to aid the readers with the implementations on computers. The book will be useful for mathematicians, statisticians, computer scientists, and data analysts. It is also a valuable handbook for other practitioners who have a basic background in mathematics, statistics and/or computer algorithms, like internet search engine designers, physicists, geologists, electronic engineers, and economists. Jianzhong Wang is a Professor of Mathematics at Sam Houston State University, U.S.A. 356 pp. Englisch. Buchnummer des Verkäufers 9783642274961

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Buchbeschreibung Springer-Verlag Gmbh Mrz 2012, 2012. Buch. Buchzustand: Neu. 240x168x28 mm. Neuware - 'Geometric Structure of High-Dimensional Data and Dimensionality Reduction' adopts data geometry as a framework to address various methods of dimensionality reduction. In addition to the introduction to well-known linear methods, the book moreover stresses the recently developed nonlinear methods and introduces the applications of dimensionality reduction in many areas, such as face recognition, image segmentation, data classification, data visualization, and hyperspectral imagery data analysis. Numerous tables and graphs are included to illustrate the ideas, effects, and shortcomings of the methods. MATLAB code of all dimensionality reduction algorithms is provided to aid the readers with the implementations on computers. The book will be useful for mathematicians, statisticians, computer scientists, and data analysts. It is also a valuable handbook for other practitioners who have a basic background in mathematics, statistics and/or computer algorithms, like internet search engine designers, physicists, geologists, electronic engineers, and economists. Jianzhong Wang is a Professor of Mathematics at Sam Houston State University, U.S.A. 356 pp. Englisch. Buchnummer des Verkäufers 9783642274961

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Buchbeschreibung Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, 2012. HRD. Buchzustand: New. New Book.Shipped from US within 10 to 14 business days. Established seller since 2000. Buchnummer des Verkäufers IB-9783642274961

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Buchbeschreibung Springer. Hardcover. Buchzustand: New. Hardcover. 365 pages. Dimensions: 9.3in. x 6.3in. x 1.0in.Geometric Structure of High-Dimensional Data and Dimensionality Reduction adopts data geometry as a framework to address various methods of dimensionality reduction. In addition to the introduction to well-known linear methods, the book moreover stresses the recently developed nonlinear methods and introduces the applications of dimensionality reduction in many areas, such as face recognition, image segmentation, data classification, data visualization, and hyperspectral imagery data analysis. Numerous tables and graphs are included to illustrate the ideas, effects, and shortcomings of the methods. MATLAB code of all dimensionality reduction algorithms is provided to aid the readers with the implementations on computers. The book will be useful for mathematicians, statisticians, computer scientists, and data analysts. It is also a valuable handbook for other practitioners who have a basic background in mathematics, statistics andor computer algorithms, like internet search engine designers, physicists, geologists, electronic engineers, and economists. Jianzhong Wang is a Professor of Mathematics at Sam Houston State University, U. S. A. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Hardcover. Buchnummer des Verkäufers 9783642274961

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