國立虎尾科技大學 |

Robust representation for data analytics = models and applications /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Robust representation for data analytics/ by Sheng Li, Yun Fu.
其他題名:	models and applications /
作者:	Li, Sheng.
其他作者:	Fu, Yun.
出版者:	Cham :Springer International Publishing : : 2017.,
面頁冊數:	xi, 224 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Knowledge representation (Information theory) -
電子資源:	http://dx.doi.org/10.1007/978-3-319-60176-2
ISBN:	9783319601762

Robust representation for data analytics = models and applications /
Li, Sheng.

Robust representation for data analyticsmodels and applications /[electronic resource] :by Sheng Li, Yun Fu. - Cham :Springer International Publishing :2017. - xi, 224 p. :ill., digital ;24 cm. - Advanced information and knowledge processing,1610-3947. - Advanced information and knowledge processing..

Introduction -- Fundamentals of Robust Representations -- Part 1: Robust Representation Models -- Robust Graph Construction -- Robust Subspace Learning -- Robust Multi-View Subspace Learning -- Part 11: Applications -- Robust Representations for Collaborative Filtering -- Robust Representations for Response Prediction -- Robust Representations for Outlier Detection -- Robust Representations for Person Re-Identification -- Robust Representations for Community Detection -- Index.

This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary. Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.

ISBN: 9783319601762

Standard No.: 10.1007/978-3-319-60176-2doiSubjects--Topical Terms:

567038
Knowledge representation (Information theory)

LC Class. No.: Q387

Dewey Class. No.: 006.332

Robust representation for data analytics = models and applications /
LDR:02424nam a2200337 a 4500 001 923445
003 DE-He213
005 20180313133248.0
006 m d
007 cr nn 008maaau
008 190625s2017 gw s 0 eng d
020 $a 9783319601762 $q (electronic bk.)
020 $a 9783319601755 $q (paper)
024 7 $a 10.1007/978-3-319-60176-2 $2 doi
035 $a 978-3-319-60176-2
040 $a GP $c GP
041 0 $a eng
050 4 $a Q387
072 7 $a UNF $2 bicssc
072 7 $a UYQE $2 bicssc
072 7 $a COM021030 $2 bisacsh
082 0 4 $a 006.332 $2 23
090 $a Q387 $b .L693 2017
100 1 $a Li, Sheng. $3 1189340
245 1 0 $a Robust representation for data analytics $h [electronic resource] : $b models and applications / $c by Sheng Li, Yun Fu.
260 $a Cham : $c 2017. $b Springer International Publishing : $b Imprint: Springer,
300 $a xi, 224 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advanced information and knowledge processing, $x 1610-3947
505 0 $a Introduction -- Fundamentals of Robust Representations -- Part 1: Robust Representation Models -- Robust Graph Construction -- Robust Subspace Learning -- Robust Multi-View Subspace Learning -- Part 11: Applications -- Robust Representations for Collaborative Filtering -- Robust Representations for Response Prediction -- Robust Representations for Outlier Detection -- Robust Representations for Person Re-Identification -- Robust Representations for Community Detection -- Index.
520 $a This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary. Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.
650 0 $a Knowledge representation (Information theory) $3 567038
650 0 $a Big data. $3 981821
650 1 4 $a Computer Science. $3 593922
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 593924
650 2 4 $a Pattern Recognition. $3 669796
650 2 4 $a Image Processing and Computer Vision. $3 670819
700 1 $a Fu, Yun. $3 843593
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a Advanced information and knowledge processing. $3 632456
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-60176-2
950 $a Computer Science (Springer-11645)