國立虎尾科技大學 |

Unsupervised Feature Extraction Applied to Bioinformatics = A PCA Based and TD Based Approach /

Record Type:	Language materials, printed : 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.
Description:	XVIII, 321 p. 111 illus., 94 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Electrical engineering. -
Online resource:	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. - 1st ed. 2020. - XVIII, 321 p. 111 illus., 94 illus. in color.online resource. - 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:

596380
Electrical engineering.

LC Class. No.: TK1-9971

Dewey Class. No.: 621.382

Unsupervised Feature Extraction Applied to Bioinformatics = A PCA Based and TD Based Approach /
LDR:02886nam a22004095i 4500 001 1020271
003 DE-He213
005 20200704051005.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030224561 $9 978-3-030-22456-1
024 7 $a 10.1007/978-3-030-22456-1 $2 doi
035 $a 978-3-030-22456-1
050 4 $a TK1-9971
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
072 7 $a TJK $2 thema
082 0 4 $a 621.382 $2 23
100 1 $a Taguchi, Y-h. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1315714
245 1 0 $a Unsupervised Feature Extraction Applied to Bioinformatics $h [electronic resource] : $b A PCA Based and TD Based Approach / $c by Y-h. Taguchi.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XVIII, 321 p. 111 illus., 94 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Unsupervised and Semi-Supervised Learning, $x 2522-848X
505 0 $a 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.
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.
650 0 $a Electrical engineering. $3 596380
650 0 $a Bioinformatics. $3 583857
650 0 $a Signal processing. $3 561459
650 0 $a Image processing. $3 557495
650 0 $a Speech processing systems. $3 564428
650 0 $a Pattern recognition. $3 1253525
650 0 $a Data mining. $3 528622
650 1 4 $a Communications Engineering, Networks. $3 669809
650 2 4 $a Computational Biology/Bioinformatics. $3 677363
650 2 4 $a Signal, Image and Speech Processing. $3 670837
650 2 4 $a Pattern Recognition. $3 669796
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030224554
776 0 8 $i Printed edition: $z 9783030224578
776 0 8 $i Printed edition: $z 9783030224585
830 0 $a Unsupervised and Semi-Supervised Learning, $x 2522-848X $3 1304411
856 4 0 $u https://doi.org/10.1007/978-3-030-22456-1
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)