Robust Representation for Data Analytics is popular PDF and ePub book, written by Sheng Li in 2017-08-09, it is a fantastic choice for those who relish reading online the Computers genre. Let's immerse ourselves in this engaging Computers book by exploring the summary and details provided below. Remember, Robust Representation for Data Analytics can be Read Online from any device for your convenience.
Robust Representation for Data Analytics Book PDF Summary
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.
Detail Book of Robust Representation for Data Analytics PDF
- Author : Sheng Li
- Release : 09 August 2017
- Publisher : Springer
- ISBN : 9783319601762
- Genre : Computers
- Total Page : 224 pages
- Language : English
- PDF File Size : 14,5 Mb
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