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Avançado
Artigos
Capa

Robust Representation for Data Analytics

Fu, Yun
SPRINGER
08 / 2017
9783319601755
978-33-1960-175-5
Inglês
Advanced Information and Knowledge Processing
Mineria de datos (''data mining'')
Inteligencia artificial
Ingles

Sinopse

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.nbsp;Robust Representations for Data Analyticsnbsp;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.