Robust Representation for Data Analytics
Models and Applications
Published on 9. August 2017
XI, 224 pages
978-3-319-60176-2 (ISBN)
Description
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.
More details
Series
Edition
1st ed. 2017
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
XI, 224 p. 52 illus., 49 illus. in color.
File size
5,03 MB
ISBN-13
978-3-319-60176-2 (9783319601762)
DOI
10.1007/978-3-319-60176-2
Schweitzer Classification
Other editions
Additional editions
Book
08/2017
Springer
€128.39
Shipment within 10-15 days
Content
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.
