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Robust representation for data analy...

Fu, Yun.

Robust representation for data analyticsmodels and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Robust representation for data analyticsby 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$q(electronic bk.)

Robust representation for data analyticsmodels 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$q(electronic bk.)

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

226484
Knowledge representation (Information theory)

LC Class. No.: Q387

Dewey Class. No.: 006.332

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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.
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