Unsupervised Feature Extraction Applied to Bioinformatics - A PCA Based and TD Based Approach (Hardcover, 1st ed. 2020)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Unsupervised and Semi-Supervised Learning
Release date:	September 2019
First published:	2020
Authors:	Y-H. Taguchi
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	321
Edition:	1st ed. 2020
ISBN-13:	978-3-03-022455-4
Categories:	Books > Science & Mathematics > Biology, life sciences > Molecular biology Books > Computing & IT > Applications of computing > Pattern recognition Books > Science & Mathematics > Biology, life sciences > Life sciences: general issues > General Books > Computing & IT > Applications of computing > Databases > Data mining Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Applied optics > General
LSN:	3-03-022455-4
Barcode:	9783030224554

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