Robust Representation for Data Analytics

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.

Enriches understanding of robust feature representations Explains how to develop robust data mining models Reinforces robust representation principles with real-world practice

Inhalt
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.