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Robust Representation for Data Analytics: Models and Applications 2017 Edition
Contributor(s): Li, Sheng (Author), Fu, Yun (Author)

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 ISBN: 331960175X     ISBN-13: 9783319601755
Publisher: Springer
OUR PRICE: $123.49  

Binding Type: Hardcover - See All Available Formats & Editions
Published: August 2017
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Additional Information
BISAC Categories:
- Computers | Databases - Data Mining
- Computers | Computer Vision & Pattern Recognition
- Computers | Computer Graphics
Dewey: 006.3
Series: Advanced Information and Knowledge Processing
Physical Information: 0.66" H x 6.54" W x 9.61" L (1.16 lbs) 224 pages
 
Descriptions, Reviews, Etc.
Publisher 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.
 
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