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.
Publisher: Springer International Publishing AG
Number of pages: 224
Weight: 5221 g
Dimensions: 235 x 155 mm
Edition: 1st ed. 2017
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