Robust Representation for Data Analytics - Models and Applications (Hardcover, 1st ed. 2017)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Advanced Information and Knowledge Processing
Release date:	August 2017
First published:	2017
Authors:	Sheng Li • Yun Fu
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	224
Edition:	1st ed. 2017
ISBN-13:	978-3-319-60175-5
Categories:	Books > Computing & IT > Applications of computing > Pattern recognition Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Image processing > General
LSN:	3-319-60175-X
Barcode:	9783319601755

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