Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach (Unsupervised and Semi-Supervised Learning)

Taguchi, Y-h.

ISBN 10: 3030224589 / ISBN 13: 9783030224585

Edité par Springer, 2020

A propos de cet article

Print on Demand pp. XVIII, 321 111 illus., 94 illus. in color.

N° de réf. du vendeur 379341641

Détails bibliographiques

• Titre: Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach (Unsupervised and Semi-Supervised Learning)
• Éditeur: Springer
• Date de parution: 2020
• Langue: anglais
• ISBN à 10 chiffres: 3030224589
• ISBN à 13 chiffres: 9783030224585
• Reliure: Couverture souple
• État de l'article: New

A propos de ce titre

Synopsis

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.

À propos de l?auteur

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.