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Unsupervised feature extraction appl...

Taguchi, Y-h.

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Unsupervised feature extraction applied to bioinformatics = a PCA based and TD based approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Unsupervised feature extraction applied to bioinformatics/ by Y-h. Taguchi.
Reminder of title:	a PCA based and TD based approach /
Author:	Taguchi, Y-h.
Published:	Cham :Springer International Publishing : : 2024.,
Description:	xxii, 533 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction to linear algebra -- Matrix factorization -- Tensor decompositions -- PCA based unsupervised FE -- TD based unsupervised FE -- Application of PCA based unsupervised FE to bioinformatics -- Application of TD based unsupervised FE to bioinformatics -- Theoretical investigation of TD and PCA based unsupervised FE.
Contained By:	Springer Nature eBook
Subject:	Principal components analysis. -
Online resource:	https://doi.org/10.1007/978-3-031-60982-4
ISBN:	9783031609824

Unsupervised feature extraction applied to bioinformatics = a 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. - Second edition. - Cham :Springer International Publishing :2024. - xxii, 533 p. :ill., digital ;24 cm. - Unsupervised and semi-supervised learning,2522-8498. - Unsupervised and semi-supervised learning..

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

This updated 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 tensor 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: 9783031609824

Standard No.: 10.1007/978-3-031-60982-4doiSubjects--Topical Terms:

565921
Principal components analysis.

LC Class. No.: QA278.5 / .T34 2024

Dewey Class. No.: 519.5354

Unsupervised feature extraction applied to bioinformatics = a PCA based and TD based approach /
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505 0 $a Introduction to linear algebra -- Matrix factorization -- Tensor decompositions -- PCA based unsupervised FE -- TD based unsupervised FE -- Application of PCA based unsupervised FE to bioinformatics -- Application of TD based unsupervised FE to bioinformatics -- Theoretical investigation of TD and PCA based unsupervised FE.
520 $a This updated 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 tensor 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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