Robust Representation for Data Analytics: Models and Applications - Yun Fu,Sheng Li
-35% su kodu BOOKS
Pristatymas per 17-23 d.d.
30 dienų grąžinimo politika
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 dictiona ... Visas aprašymas
Jums taip pat gali patikti
Aprašymas
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.
Daugiau informacijos
| Autorius | Yun Fu, Sheng Li |
|---|---|
| Leidėjas | Springer Nature Switzerland |
| Series | Advanced Information and Knowledge Processing |
| Išleidimo metai | 2017 |
| Viršelio tipas | Kieti viršeliai |
| EAN | 9783319601755 |