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Nonlinear Dimensionality Reduction (Information Science and Statistics)
Book 7 of 20: Information Science and StatisticsLanguage: English
Published by Springer (edition 2007), 2007
ISBN 10: 0387393501 ISBN 13: 9780387393506
Hardcover. Condition: Very Good. 2007. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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Nonlinear Dimensionality Reduction (Hardcover)
Book 7 of 20: Information Science and StatisticsLanguage: English
Published by Springer-Verlag New York Inc., New York, NY, 2007
ISBN 10: 0387393501 ISBN 13: 9780387393506
Hardcover. Condition: new. Hardcover. Methods of dimensionality reduction provide a way to understand and visualize the structure of complex data sets. Traditional methods like principal component analysis and classical metric multidimensional scaling suffer from being based on linear models. Until recently, very few methods were able to reduce the data dimensionality in a nonlinear way. However, since the late nineties, many new methods have been developed and nonlinear dimensionality reduction, also called manifold learning, has become a hot topic. New advances that account for this rapid growth are, e.g. the use of graphs to represent the manifold topology, and the use of new metrics like the geodesic distance. In addition, new optimization schemes, based on kernel techniques and spectral decomposition, have lead to spectral embedding, which encompasses many of the recently developed methods. This book describes existing and advanced methods to reduce the dimensionality of numerical databases. For each method, the description starts from intuitive ideas, develops the necessary mathematical details, and ends by outlining the algorithmic implementation. Methods are compared with each other with the help of different illustrative examples. The purpose of the book is to summarize clear facts and ideas about well-known methods as well as recent developments in the topic of nonlinear dimensionality reduction. With this goal in mind, methods are all described from a unifying point of view, in order to highlight their respective strengths and shortcomings. The book is primarily intended for statisticians, computer scientists and data analysts. It is also accessible to other practitioners having a basic background in statistics and/or computational learning, like psychologists (in psychometry) and economists. However, since the late nineties, many new methods have been developed and nonlinear dimensionality reduction, also called manifold learning, has become a hot topic. The purpose of the book is to summarize clear facts and ideas about well-known methods as well as recent developments in the topic of nonlinear dimensionality reduction. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Nonlinear Dimensionality Reduction
Book 7 of 20: Information Science and StatisticsSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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Nonlinear Dimensionality Reduction (Information Science and Statistics)
Book 7 of 20: Information Science and StatisticsSeller: Ria Christie Collections, Uxbridge, United Kingdom
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Nonlinear Dimensionality Reduction (Information Science and Statistics)
Book 7 of 20: Information Science and StatisticsSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 117.36
£ 11.98 shipping
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Condition: As New. Unread book in perfect condition.
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Nonlinear Dimensionality Reduction
Book 7 of 20: Information Science and StatisticsSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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£ 130.26
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Add to basketCondition: As New. Unread book in perfect condition.
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Condition: New. pp. xviii + 310 1st Edition.
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Condition: New. pp. xviii + 310 Illus.
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Condition: New. pp. xviii + 310.
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Paperback. Condition: Brand New. 328 pages. 9.00x6.00x0.77 inches. In Stock.
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Hardcover. Condition: Brand New. 1st edition. 308 pages. 9.50x6.25x1.00 inches. In Stock.
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Nonlinear Dimensionality Reduction (Hardcover)
Book 7 of 20: Information Science and StatisticsLanguage: English
Published by Springer-Verlag New York Inc., New York, NY, 2007
ISBN 10: 0387393501 ISBN 13: 9780387393506
Hardcover. Condition: new. Hardcover. Methods of dimensionality reduction provide a way to understand and visualize the structure of complex data sets. Traditional methods like principal component analysis and classical metric multidimensional scaling suffer from being based on linear models. Until recently, very few methods were able to reduce the data dimensionality in a nonlinear way. However, since the late nineties, many new methods have been developed and nonlinear dimensionality reduction, also called manifold learning, has become a hot topic. New advances that account for this rapid growth are, e.g. the use of graphs to represent the manifold topology, and the use of new metrics like the geodesic distance. In addition, new optimization schemes, based on kernel techniques and spectral decomposition, have lead to spectral embedding, which encompasses many of the recently developed methods. This book describes existing and advanced methods to reduce the dimensionality of numerical databases. For each method, the description starts from intuitive ideas, develops the necessary mathematical details, and ends by outlining the algorithmic implementation. Methods are compared with each other with the help of different illustrative examples. The purpose of the book is to summarize clear facts and ideas about well-known methods as well as recent developments in the topic of nonlinear dimensionality reduction. With this goal in mind, methods are all described from a unifying point of view, in order to highlight their respective strengths and shortcomings. The book is primarily intended for statisticians, computer scientists and data analysts. It is also accessible to other practitioners having a basic background in statistics and/or computational learning, like psychologists (in psychometry) and economists. However, since the late nineties, many new methods have been developed and nonlinear dimensionality reduction, also called manifold learning, has become a hot topic. The purpose of the book is to summarize clear facts and ideas about well-known methods as well as recent developments in the topic of nonlinear dimensionality reduction. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Paperback. Condition: Like New. Like New. book.
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Condition: new. Questo è un articolo print on demand.
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Hardcover. Condition: Brand New. 1st edition. 308 pages. 9.50x6.25x1.00 inches. In Stock. This item is printed on demand.
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Nonlinear Dimensionality Reduction
Book 7 of 20: Information Science and StatisticsLanguage: English
Published by Springer New York Dez 2007, 2007
ISBN 10: 0387393501 ISBN 13: 9780387393506
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Methods of dimensionality reduction provide a way to understand and visualize the structure of complex data sets. Traditional methods like principal component analysis and classical metric multidimensional scaling suffer from being based on linear models. Until recently, very few methods were able to reduce the data dimensionality in a nonlinear way. However, since the late nineties, many new methods have been developed and nonlinear dimensionality reduction, also called manifold learning, has become a hot topic. New advances that account for this rapid growth are, e.g. the use of graphs to represent the manifold topology, and the use of new metrics like the geodesic distance. In addition, new optimization schemes, based on kernel techniques and spectral decomposition, have lead to spectral embedding, which encompasses many of the recently developed methods. This book describes existing and advanced methods to reduce the dimensionality of numerical databases. For each method, the description starts from intuitive ideas, develops the necessary mathematical details, and ends by outlining the algorithmic implementation. Methods are compared with each other with the help of different illustrative examples. The purpose of the book is to summarize clear facts and ideas about well-known methods as well as recent developments in the topic of nonlinear dimensionality reduction. With this goal in mind, methods are all described from a unifying point of view, in order to highlight their respective strengths and shortcomings. The book is primarily intended for statisticians, computer scientists and data analysts. It is also accessible to other practitioners having a basic background in statistics and/or computational learning, like psychologists (in psychometry) and economists. 309 pp. Englisch.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Covers a wide range of methods to reduce the dimensionality of data and presents a unifying point of viewThis book describes existing and advanced methods to reduce the dimensionality of numerical databases. For each method, the description start.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Covers a wide range of methods to reduce the dimensionality of data and presents a unifying point of viewThis book describes existing and advanced methods to reduce the dimensionality of numerical databases. For each method, the description start.
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Nonlinear Dimensionality Reduction
Book 7 of 20: Information Science and StatisticsLanguage: English
Published by Springer-Verlag New York Inc., 2010
ISBN 10: 1441922881 ISBN 13: 9781441922885
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
£ 139.11
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Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketPaperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
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Nonlinear Dimensionality Reduction
Book 7 of 20: Information Science and StatisticsLanguage: English
Published by Springer-Verlag New York Inc., 2007
ISBN 10: 0387393501 ISBN 13: 9780387393506
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
£ 140.07
£ 19.32 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketHardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
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Buch. Condition: Neu. Nonlinear Dimensionality Reduction | John A. Lee (u. a.) | Buch | Information Science and Statistics | xvii | Englisch | 2007 | Springer | EAN 9780387393506 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
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Condition: New. Print on Demand pp. xviii + 310 8 Illus.
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Condition: New. PRINT ON DEMAND pp. xviii + 310.
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Condition: New. PRINT ON DEMAND pp. xviii + 310.
