Robust Representation for Data Analytics Models and Applications /

• Κύριοι συγγραφείς: Li, Sheng (Συγγραφέας), Fu, Yun (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Σειρά: Advanced Information and Knowledge Processing,
• Θέματα: Computer science.; Data mining.; Artificial intelligence.; Image processing.; Pattern recognition.; Computer Science.; Data Mining and Knowledge Discovery.; Artificial Intelligence (incl. Robotics).; Pattern Recognition.; Image Processing and Computer Vision.

 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.