Read Robust Representation for Data Analytics: Models and Applications (Advanced Information and Knowledge Processing)

Robust Representation for Data Analytics: Models and Applications (Advanced Information and Knowledge Processing)

Sheng Li

4.9/5 (24177 ratings)

Description: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.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Robust Representation for Data Analytics: Models and Applications (Advanced Information and Knowledge Processing). To get started finding Robust Representation for Data Analytics: Models and Applications (Advanced Information and Knowledge Processing), you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Format: PDF, EPUB & Kindle Edition

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ISBN: 3319601768

Description: 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.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Robust Representation for Data Analytics: Models and Applications (Advanced Information and Knowledge Processing). To get started finding Robust Representation for Data Analytics: Models and Applications (Advanced Information and Knowledge Processing), you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Robust Representation for Data Analytics: Models and Applications (Advanced Information and Knowledge Processing)

Robust Representation for Data Analytics: Models and Applications (Advanced Information and Knowledge Processing)

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