國立高雄大學圖資館 |

Multi-aspect learningmethods and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multi-aspect learningby Richi Nayak, Khanh Luong.
Reminder of title:	methods and applications /
Author:	Nayak, Richi.
other author:	Luong, Khanh.
Published:	Cham :Springer International Publishing :2023.
Description:	viii, 184 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-031-33560-0
ISBN:	9783031335600$q(electronic bk.)

Multi-aspect learningmethods and applications /
Nayak, Richi.

Multi-aspect learningmethods and applications /[electronic resource] :by Richi Nayak, Khanh Luong. - Cham :Springer International Publishing :2023. - viii, 184 p. :ill. (some col.), digital ;24 cm. - Intelligent systems reference library,v. 2421868-4408 ;. - Intelligent systems reference library ;v.24..

1 Multi-Aspect Data Learning: Overview, Challenges and Approaches -- 2 Non-negative Matrix Factorization-Based Multi-Aspect Data Clustering -- 3 NMF and Manifold Learning for Multi-Aspect Data -- 4 Subspace Learning for Multi-Aspect Data -- 5 Spectral Clustering on Multi-Aspect Data -- 6 Learning Consensus and Complementary Information for Multi-Aspect Data Clustering -- 7 Deep Learning-Based Methods for Multi-Aspect Data Clustering.

This book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clustering and their applications in various domains. This is the first book to systematically review multi-aspect data learning, incorporating a range of concepts and applications. Additionally, it is the first to comprehensively investigate manifold learning for dimensionality reduction in multi-view data learning. The book presents the latest advances in matrix factorization, subspace clustering, spectral clustering and deep learning methods, with a particular emphasis on the challenges and characteristics of multi-aspect data. Each chapter includes a thorough discussion of state-of-the-art of multi-aspect data learning methods and important research gaps. The book provides readers with the necessary foundational knowledge to apply these methods to new domains and applications, as well as inspire new research in this emerging field.

ISBN: 9783031335600$q(electronic bk.)

Standard No.: 10.1007/978-3-031-33560-0doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Multi-aspect learningmethods and applications /
LDR:02602nmm a2200337 a 4500 001 648717
003 DE-He213
005 20230727113449.0
006 m d
007 cr nn 008maaau
008 240116s2023 sz s 0 eng d
020 $a 9783031335600$q(electronic bk.)
020 $a 9783031335594$q(paper)
024 7 $a 10.1007/978-3-031-33560-0 $2 doi
035 $a 978-3-031-33560-0
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UN $2 bicssc
072 7 $a COM018000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .N331 2023
100 1 $a Nayak, Richi. $3 279177
245 1 0 $a Multi-aspect learning $h [electronic resource] : $b methods and applications / $c by Richi Nayak, Khanh Luong.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a viii, 184 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Intelligent systems reference library, $x 1868-4408 ; $v v. 242
505 0 $a 1 Multi-Aspect Data Learning: Overview, Challenges and Approaches -- 2 Non-negative Matrix Factorization-Based Multi-Aspect Data Clustering -- 3 NMF and Manifold Learning for Multi-Aspect Data -- 4 Subspace Learning for Multi-Aspect Data -- 5 Spectral Clustering on Multi-Aspect Data -- 6 Learning Consensus and Complementary Information for Multi-Aspect Data Clustering -- 7 Deep Learning-Based Methods for Multi-Aspect Data Clustering.
520 $a This book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clustering and their applications in various domains. This is the first book to systematically review multi-aspect data learning, incorporating a range of concepts and applications. Additionally, it is the first to comprehensively investigate manifold learning for dimensionality reduction in multi-view data learning. The book presents the latest advances in matrix factorization, subspace clustering, spectral clustering and deep learning methods, with a particular emphasis on the challenges and characteristics of multi-aspect data. Each chapter includes a thorough discussion of state-of-the-art of multi-aspect data learning methods and important research gaps. The book provides readers with the necessary foundational knowledge to apply these methods to new domains and applications, as well as inspire new research in this emerging field.
650 0 $a Machine learning. $3 188639
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Machine Learning. $3 833608
650 1 4 $a Data Engineering. $3 839346
700 1 $a Luong, Khanh. $3 958517
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Intelligent systems reference library ; $v v.24. $3 558591
856 4 0 $u https://doi.org/10.1007/978-3-031-33560-0
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)