Overview
- 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
Part of the book series: Unsupervised and Semi-Supervised Learning (UNSESUL)
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Table of contents (7 chapters)
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Front Matter
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Part I
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Front Matter
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Part II
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Front Matter
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Back Matter
Keywords
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.
Authors and Affiliations
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.
Bibliographic Information
Book Title: Unsupervised Feature Extraction Applied to Bioinformatics
Book Subtitle: A PCA Based and TD Based Approach
Authors: Y-h. Taguchi
Series Title: Unsupervised and Semi-Supervised Learning
DOI: https://doi.org/10.1007/978-3-030-22456-1
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-22455-4Published: 05 September 2019
Softcover ISBN: 978-3-030-22458-5Published: 05 September 2020
eBook ISBN: 978-3-030-22456-1Published: 23 August 2019
Series ISSN: 2522-848X
Series E-ISSN: 2522-8498
Edition Number: 1
Number of Pages: XVIII, 321
Number of Illustrations: 17 b/w illustrations, 94 illustrations in colour
Topics: Communications Engineering, Networks, Computational Biology/Bioinformatics, Signal, Image and Speech Processing, Bioinformatics, Pattern Recognition, Data Mining and Knowledge Discovery