Robust Representation for Data Analytics: Models and Applications - Couverture rigide

Li, Sheng; Fu, Yun

Couverture rigide

ISBN 10 : 331960175X ISBN 13 : 9783319601755

Editeur : Springer International Publishing AG, 2017

Synopsis

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.

Détails de l'édition

• Éditeur: Springer International Publishing AG
• Date d'édition: 2017
• Langue: anglais
• ISBN 10: 331960175X
• ISBN 13: 9783319601755
• Reliure: Relié
• Numéro d'édition: 1
• Nombre de pages: 235
• Coordonnées du fabricant: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Allemagne