國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Unsupervised feature extraction appl...

SpringerLink (Online service)

Unsupervised feature extraction applied to bioinformaticsa 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 bioinformaticsby 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.
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$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 /
LDR:02523nmm a2200337 a 4500 001 593145
003 DE-He213
005 20200704051005.0
006 m d
007 cr nn 008maaau
008 210727s2020 sz s 0 eng d
020 $a 9783030224561$q(electronic bk.)
020 $a 9783030224554$q(paper)
024 7 $a 10.1007/978-3-030-22456-1 $2 doi
035 $a 978-3-030-22456-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA278.5 $b .T34 2020
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
072 7 $a TJK $2 thema
082 0 4 $a 519.5354 $2 23
090 $a QA278.5 $b .T128 2020
100 1 $a Taguchi, Y-h. $3 884405
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.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xviii, 321 p. : $b ill., digital ; $c 24 cm.
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 Principal components analysis. $3 182575
650 0 $a Calculus of tensors. $3 210256
650 0 $a Bioinformatics. $3 194415
650 1 4 $a Communications Engineering, Networks. $3 273745
650 2 4 $a Computational Biology/Bioinformatics. $3 274833
650 2 4 $a Signal, Image and Speech Processing. $3 273768
650 2 4 $a Pattern Recognition. $3 273706
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Unsupervised and semi-supervised learning. $3 834422
856 4 0 $u https://doi.org/10.1007/978-3-030-22456-1
950 $a Engineering (SpringerNature-11647)