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Unsupervised feature extraction applied to bioinformaticsa PCA based and TD based approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Unsupervised feature extraction applied to bioinformaticsby Y-h. Taguchi.
Reminder of title:	a PCA based and TD based approach /
Author:	Taguchi, Y-h.
Published:	Cham :Springer International Publishing :2020.
Description:	xviii, 321 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Principal components analysis.
Online resource:	https://doi.org/10.1007/978-3-030-22456-1
ISBN:	9783030224561$q(electronic bk.)

Unsupervised feature extraction applied to bioinformaticsa PCA based and TD based approach /
Taguchi, Y-h.

Unsupervised feature extraction applied to bioinformaticsa PCA based and TD based approach /[electronic resource] :by Y-h. Taguchi. - Cham :Springer International Publishing :2020. - xviii, 321 p. :ill., digital ;24 cm. - Unsupervised and semi-supervised learning,2522-848X. - Unsupervised and semi-supervised learning..

Introduction to linear algebra -- Matrix factorization -- Tensor decompositions -- PCA based unsupervised FE -- TD based unsupervised FE -- Application of PCA/TD based unsupervised FE to bioinformatics -- Application of TD based unsupervised FE to bioinformatics.

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.

ISBN: 9783030224561$q(electronic bk.)

Standard No.: 10.1007/978-3-030-22456-1doiSubjects--Topical Terms:

182575
Principal components analysis.

LC Class. No.: QA278.5 / .T34 2020

Dewey Class. No.: 519.5354

Unsupervised feature extraction applied to bioinformaticsa PCA based and TD based approach /
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520 $a 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.
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