Authors:
- Enriches understanding of robust feature representations
- Explains how to develop robust data mining models
- Reinforces robust representation principles with real-world practice
Part of the book series: Advanced Information and Knowledge Processing (AI&KP)
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Table of contents (10 chapters)
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Front Matter
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Robust Representation Models
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Front Matter
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Back Matter
About this book
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.
Authors and Affiliations
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Northeastern University, Boston, USA
Sheng Li
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Northeastern University , BOSTON, USA
Yun Fu
Bibliographic Information
Book Title: Robust Representation for Data Analytics
Book Subtitle: Models and Applications
Authors: Sheng Li, Yun Fu
Series Title: Advanced Information and Knowledge Processing
DOI: https://doi.org/10.1007/978-3-319-60176-2
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2017
Hardcover ISBN: 978-3-319-60175-5Published: 29 August 2017
Softcover ISBN: 978-3-319-86796-0Published: 04 August 2018
eBook ISBN: 978-3-319-60176-2Published: 09 August 2017
Series ISSN: 1610-3947
Series E-ISSN: 2197-8441
Edition Number: 1
Number of Pages: XI, 224
Number of Illustrations: 3 b/w illustrations, 49 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Artificial Intelligence, Pattern Recognition, Image Processing and Computer Vision