Robust representation for data analytics : models and applications

✍ Scribed by Fu, Yun; Li, Sheng

• Publisher: Springer
• Year: 2017
• Tongue: English
• Leaves: 229
• Series: Advanced information and knowledge processing
• Category: Library

✦ 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. Read more...

Abstract: 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

Front Matter ....Pages i-xi
Introduction (Sheng Li, Yun Fu)....Pages 1-5
Front Matter ....Pages 7-7
Fundamentals of Robust Representations (Sheng Li, Yun Fu)....Pages 9-16
Robust Graph Construction (Sheng Li, Yun Fu)....Pages 17-44
Robust Subspace Learning (Sheng Li, Yun Fu)....Pages 45-71
Robust Multi-view Subspace Learning (Sheng Li, Yun Fu)....Pages 73-93
Robust Dictionary Learning (Sheng Li, Yun Fu)....Pages 95-119
Front Matter ....Pages 121-121
Robust Representations for Collaborative Filtering (Sheng Li, Yun Fu)....Pages 123-146
Robust Representations for Response Prediction (Sheng Li, Yun Fu)....Pages 147-174
Robust Representations for Outlier Detection (Sheng Li, Yun Fu)....Pages 175-201
Robust Representations for Person Re-identification (Sheng Li, Yun Fu)....Pages 203-222
Back Matter ....Pages 223-224

✦ Subjects

Knowledge representation (Information theory);Big data;COMPUTERS / General;Computer Science;Data Mining and Knowledge Discovery;Artificial Intelligence (incl. Robotics);Pattern Recognition;Image Processing and Computer Vision