9783030224585 - Unsupervised Feature Extraction Applied to Bioinformatics: a Pca Based and Td Based Approach Unsupervised and Semi-supervised Learning by Taguchi, Y-h (11 results)

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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tenor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics.Allows readers to analyze data sets with small samples and many features;Provides a fast algorithm, based upon linear algebra, to analyze big data;Includes several applications to multi-view data analyses, with a focus on bioinformatics.

Condition: New. pp. XVIII, 321 111 illus., 94 illus. in color. 1 Edition NO-PA16APR2015-KAP.

Taschenbuch. Condition: Neu. Neuware -This book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tenor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 340 pp. Englisch.

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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Allows readers to analyze data sets with small samples and many featuresProvides a fast algorithm, based upon linear algebra, to analyze big dataIncludes several applications to multi-view data analyses, with a focus on bioinf.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tenor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics.Allows readers to analyze data sets with small samples and many features;Provides a fast algorithm, based upon linear algebra, to analyze big data;Includes several applications to multi-view data analyses, with a focus on bioinformatics. 340 pp. Englisch.

Condition: New. Print on Demand pp. XVIII, 321 111 illus., 94 illus. in color.

Condition: New. PRINT ON DEMAND pp. XVIII, 321 111 illus., 94 illus. in color.