Robust Representation for Data Analytics Models and Applications
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- Hardcover
- Taschenbuch ausgewählt
- eBook
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Sprache:Englisch
Fr. 162.00
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
04.08.2018
Abbildungen
XI, 224 p. 52 illus., 49 illus. in color.
Verlag
SpringerSeitenzahl
224
Maße (L/B/H)
23.5/15.5/1.3 cm
Gewicht
408 g
Auflage
Softcover reprint of the original 1st ed. 2017
Sprache
Englisch
ISBN
978-3-319-86796-0
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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