This updated 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 tensor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics.
"synopsis" may belong to another edition of this title.
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 150 journal papers, book chapters and papers in conference proceedings and was recognized as top 2% scientist of the world in 3rd consecutive years (2021, 2022, 2023) according to analysis of Stanford University, USA and report of Elsevier in bioinformatics.
This updated 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 tensor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics.
"About this title" may belong to another edition of this title.
Seller: Basi6 International, Irving, TX, U.S.A.
Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Seller Inventory # ABEJUNE24-19601
Quantity: 1 available
Seller: Books Puddle, New York, NY, U.S.A.
Condition: New. Second Edition 2024 NO-PA16APR2015-KAP. Seller Inventory # 26402091650
Quantity: 1 available
Seller: Majestic Books, Hounslow, United Kingdom
Condition: New. Seller Inventory # 394318173
Quantity: 1 available
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # S0-9783031609817
Quantity: 1 available
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. Seller Inventory # 18402091656
Quantity: 1 available
Seller: Revaluation Books, Exeter, United Kingdom
Hardcover. Condition: Brand New. 2nd edition. 555 pages. 9.25x6.10x9.21 inches. In Stock. This item is printed on demand. Seller Inventory # __3031609816
Quantity: 1 available
Seller: moluna, Greven, Germany
Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This updated 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 . Seller Inventory # 1592583110
Quantity: Over 20 available
Seller: Grand Eagle Retail, Fairfield, OH, U.S.A.
Hardcover. Condition: new. Hardcover. This updated 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 tensor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics. This updated book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. 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 tensor decomposition. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9783031609817
Quantity: 1 available
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This updated 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 tensor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics. 527 pp. Englisch. Seller Inventory # 9783031609817
Quantity: 2 available
Seller: California Books, Miami, FL, U.S.A.
Condition: New. Seller Inventory # I-9783031609817
Quantity: Over 20 available