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.
| ISBN: | 9783319867960 |
| Publication date: | 4th August 2018 |
| Author: | Sheng Li, Yun Fu |
| Publisher: | Springer an imprint of Springer International Publishing |
| Format: | Paperback |
| Pagination: | 224 pages |
| Series: | Advanced Information and Knowledge Processing |
| Genres: |
Data mining Expert systems / knowledge-based systems Pattern recognition Computer vision Artificial intelligence |
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.
Robust Representation for Data Analytics features in the following genres: Data mining, Expert systems / knowledge-based systems, Pattern recognition, Computer vision, Artificial intelligence
Robust Representation for Data Analytics is available in Paperback, Hardback
Robust Representation for Data Analytics was written by Sheng Li, Yun Fu and published by Springer an imprint of Springer International Publishing
Robust Representation for Data Analytics has 224 pages
Yes it is part of Advanced Information and Knowledge Processing series
£98.99