Robust Representation for Data Analytics | Models and Applications

Sheng Li (u. a.)

ISBN 10: 331960175X / ISBN 13: 9783319601755

Published by Springer, 2017

About this Item

Robust Representation for Data Analytics | Models and Applications | Sheng Li (u. a.) | Buch | Advanced Information and Knowledge Processing | xi | Englisch | 2017 | Springer | EAN 9783319601755 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Seller Inventory # 109687890

Bibliographic Details

• Title: Robust Representation for Data Analytics | Models and Applications
• Publisher: Springer
• Publication Date: 2017
• Language: English
• ISBN 10: 331960175X
• ISBN 13: 9783319601755
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 18D millimeters 241 millimeters width by 160 millimeters height by 18 millimeters depth
• Weight: 567 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.