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Robust Representation for Data Analytics

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Robust Representation for Data Analytics

Models and Applications
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Description

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.
Release date Australia
August 29th, 2017
Author
Pages
224
Edition
1st ed. 2017
Audience
  • Professional & Vocational
Illustrations
49 Illustrations, color; 3 Illustrations, black and white; XI, 224 p. 52 illus., 49 illus. in color.
ISBN-13
9783319601755
Product ID
26812300

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