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Robust representation for data analy...

Li, Sheng.

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Robust representation for data analytics = models and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Robust representation for data analytics/ by Sheng Li, Yun Fu.
Reminder of title:	models and applications /
Author:	Li, Sheng.
other author:	Fu, Yun.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xi, 224 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Knowledge representation (Information theory) -
Online resource:	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:

539449
Knowledge representation (Information theory)

LC Class. No.: Q387

Dewey Class. No.: 006.332

Robust representation for data analytics = models and applications /
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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.
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