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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 /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Unsupervised feature extraction applied to bioinformatics/ by Y-h. Taguchi.
其他題名:	a PCA based and TD based approach /
作者:	Taguchi, Y-h.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xviii, 321 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Principal components analysis. -
電子資源:	https://doi.org/10.1007/978-3-030-22456-1
ISBN:	9783030224561

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. - 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

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

565921
Principal components analysis.

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

Dewey Class. No.: 519.5354

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