Robust Representation for Data Analytics

Models and Applications

Sheng Li, Yun Fu (Autoren)

Buch | Softcover

XI, 224 Seiten

2018 | Softcover reprint of the original 1st ed. 2017
Springer International Publishing (Verlag)
978-3-319-86796-0 (ISBN)

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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.

Introduction.- Fundamentals of Robust Representations.- Part 1: Robust Representation Models.- Robust Graph Construction.- Robust Subspace Learning.- Robust Multi-View Subspace Learning.- Part 11: Applications.- Robust Representations for Collaborative Filtering.- Robust Representations for Response Prediction.- Robust Representations for Outlier Detection.- Robust Representations for Person Re-Identification.- Robust Representations for Community Detection.- Index.

Erscheint lt. Verlag	4.8.2018
Reihe/Serie	Advanced Information and Knowledge Processing
Zusatzinfo	XI, 224 p. 52 illus., 49 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	496 g
Themenwelt	Informatik ► Datenbanken ► Data Warehouse / Data Mining
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	Graph Construction • Multi-view learning • Outlier Detection • Robust Representations • subspace learning
ISBN-10	3-319-86796-2 / 3319867962
ISBN-13	978-3-319-86796-0 / 9783319867960
Zustand	Neuware