Unsupervised and Semi-Supervised Learning

Unsupervised Feature Extraction Applied to Bioinformatics

A PCA Based and TD Based Approach

Authors: Taguchi, Yoshihiro T.

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  • 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
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eBook 118,99 €
price for Spain (gross)
  • ISBN 978-3-030-22456-1
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  • Included format: EPUB, PDF
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  • Immediate eBook download after purchase
Hardcover 155,99 €
price for Spain (gross)
  • ISBN 978-3-030-22455-4
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  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

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.

About the authors

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. 

 

Table of contents (7 chapters)

Table of contents (7 chapters)

Buy this book

eBook 118,99 €
price for Spain (gross)
  • ISBN 978-3-030-22456-1
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 155,99 €
price for Spain (gross)
  • ISBN 978-3-030-22455-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Unsupervised Feature Extraction Applied to Bioinformatics
Book Subtitle
A PCA Based and TD Based Approach
Authors
Series Title
Unsupervised and Semi-Supervised Learning
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-22456-1
DOI
10.1007/978-3-030-22456-1
Hardcover ISBN
978-3-030-22455-4
Series ISSN
2522-848X
Edition Number
1
Number of Pages
XVIII, 321
Number of Illustrations
17 b/w illustrations, 94 illustrations in colour
Topics