Robust Representation for Data Analytics: Models and Applications

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.

Table of Contents

Chapter 1 Introduction
Part I Robust Representation Models
Chapter 2 Fundamentals Of Robust Representations
Chapter 3 Robust Graph Construction
Chapter 4 Robust Subspace Learning
Chapter 5 Robust Multi-View Subspace Learning
Chapter 6 Robust Dictionary Learning

Part II Applications
Chapter 7 Robust Representations For Collaborative Filtering
Chapter 8 Robust Representations For Response Prediction
Chapter 9 Robust Representations For Outlier Detection
Chapter 10 Robust Representations For Person Re-Identification