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Robust Representation for Data Analytics : Models and Applications (Hardcover) (Sheng Li & Yun Fu)

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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.

Genre: Computers + Internet
Series Title: Advanced Information and Knowledge Processing
Format: Hardcover
Publisher: Springer Verlag
Author: Sheng Li & Yun Fu
Language: English
Street Date: August 29, 2017
TCIN: 52944603
UPC: 9783319601755
Item Number (DPCI): 248-49-9555
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