Robust Representation for Data Analytics
Models and Applications
Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Database Management, General Computing
| Author: | Sheng Li, Yun Fu | ISBN: | 9783319601762 |
| Publisher: | Springer International Publishing | Publication: | August 9, 2017 |
| Imprint: | Springer | Language: | English |
| Author: | Sheng Li, Yun Fu |
| ISBN: | 9783319601762 |
| Publisher: | Springer International Publishing |
| Publication: | August 9, 2017 |
| Imprint: | Springer |
| Language: | English |
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.
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.
