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Unsupervised Feature Extraction Applied to Bioinformatics

A PCA Based and TD Based Approach

  • Y-h.┬áTaguchi

Part of the Unsupervised and Semi-Supervised Learning book series (UNSESUL)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Part I

    1. Front Matter
      Pages 1-1
    2. Y-h. Taguchi
      Pages 3-22
    3. Y-h. Taguchi
      Pages 23-45
    4. Y-h. Taguchi
      Pages 47-78
  3. Part II

    1. Front Matter
      Pages 79-79
    2. Y-h. Taguchi
      Pages 81-102
    3. Y-h. Taguchi
      Pages 103-116
  4. Part III

    1. Front Matter
      Pages 117-117
  5. Back Matter
    Pages 297-321

About this book

Introduction

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.

Keywords

Matrix factorization Tensor decompositions PCA based unsupervised FE TD based unsupervised FE PCA/TD based unsupervised FE Bioinformatics problems

Authors and affiliations

  • Y-h.┬áTaguchi
    • 1
  1. 1.Department of PhysicsChuo UniversityTokyoJapan

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-22456-1
  • Copyright Information Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-030-22455-4
  • Online ISBN 978-3-030-22456-1
  • Series Print ISSN 2522-848X
  • Series Online ISSN 2522-8498
  • Buy this book on publisher's site
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