| 書目名稱 | Robust Representation for Data Analytics | | 副標(biāo)題 | Models and Applicati | | 編輯 | Sheng Li,Yun Fu | | 視頻video | http://file.papertrans.cn/832/831355/831355.mp4 | | 概述 | Enriches understanding of robust feature representations.Explains how to develop robust data mining models.Reinforces robust representation principles with real-world practice | | 叢書名稱 | Advanced Information and Knowledge Processing | | 圖書封面 |  | | 描述 | 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.. | | 出版日期 | Book 2017 | | 關(guān)鍵詞 | Robust Representations; Graph Construction; Subspace Learning; Outlier Detection; Multi-view Learning | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-60176-2 | | isbn_softcover | 978-3-319-86796-0 | | isbn_ebook | 978-3-319-60176-2Series ISSN 1610-3947 Series E-ISSN 2197-8441 | | issn_series | 1610-3947 | | copyright | Springer International Publishing AG, part of Springer Nature 2017 |
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