From
Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
AbeBooks Seller since March 25, 2015
In. Seller Inventory # ria9783030224585_new
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.
About the Author:
Prof. Taguchi is currently a Professor at Department of Physics, Chuo University. Prof. Taguchi received a master degree in Statistical Physics from Tokyo Institute of Technology, Japan in 1986, and PhD degree in Non-linear Physics from Tokyo Institute of Technology, Tokyo, Japan in 1988. He worked at Tokyo Institute of Technology and Chuo University. He is with Chuo University (Tokyo, Japan) since 1997. He currently holds the Professor position at this university. His main research interests are in the area of Bioinformatics, especially, multi-omics data analysis using linear algebra. Dr. Taguchi has published a book on bioinformatics, more than 100 journal papers, book chapters and papers in conference proceedings.
Title: Unsupervised Feature Extraction Applied to ...
Publisher: Springer
Publication Date: 2020
Binding: Soft cover
Condition: New
Seller: SpringBooks, Berlin, Germany
Softcover. Condition: Very Good. 1. Auflage. Unread, some shelfwear. Immediately dispatched from Germany. Seller Inventory # CEA-2404C-HUND-01-1000XS
Quantity: 1 available
Seller: moluna, Greven, Germany
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. Seller Inventory # 448675817
Quantity: Over 20 available
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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. Seller Inventory # 9783030224585
Quantity: 2 available
Seller: AHA-BUCH GmbH, Einbeck, Germany
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. Seller Inventory # 9783030224585
Quantity: 1 available
Seller: Books Puddle, New York, NY, U.S.A.
Condition: New. pp. XVIII, 321 111 illus., 94 illus. in color. 1 Edition NO-PA16APR2015-KAP. Seller Inventory # 26384562326
Quantity: 4 available
Seller: Majestic Books, Hounslow, United Kingdom
Condition: New. Print on Demand pp. XVIII, 321 111 illus., 94 illus. in color. Seller Inventory # 379341641
Quantity: 4 available
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. PRINT ON DEMAND pp. XVIII, 321 111 illus., 94 illus. in color. Seller Inventory # 18384562332
Quantity: 4 available
Seller: Mispah books, Redhill, SURRE, United Kingdom
Paperback. Condition: New. New. book. Seller Inventory # ERICA77330302245896
Quantity: 1 available