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Robust Representation for Data Analytics - Models and Applications (Hardcover, 1st ed. 2017)
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Robust Representation for Data Analytics - Models and Applications (Hardcover, 1st ed. 2017)
Series: Advanced Information and Knowledge Processing
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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.
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