| 書(shū)目名稱 | Learning Representation for Multi-View Data Analysis | | 副標(biāo)題 | Models and Applicati | | 編輯 | Zhengming Ding,Handong Zhao,Yun Fu | | 視頻video | http://file.papertrans.cn/583/582783/582783.mp4 | | 概述 | Broadens your understanding of multi-view data analysis.Explains how to design an effective multi-view data representation model.Reinforces multi-view representation principles with real-world practic | | 叢書(shū)名稱 | Advanced Information and Knowledge Processing | | 圖書(shū)封面 |  | | 描述 | .This book equips readers to handle complex multi-view data representation, centered around several major visual applications, sharing many tips and insights through a unified learning framework. This framework is able to model most existing multi-view learning and domain adaptation, enriching readers’ understanding from their similarity, and differences based on data organization and problem?settings, as well as the research goal..A comprehensive review exhaustively provides the key recent research on multi-view data analysis, i.e., multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. More practical challenges in multi-view data analysis are discussed including incomplete, unbalanced and large-scale multi-view learning. .Learning Representation for Multi-View Data Analysis .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 2019 | | 關(guān)鍵詞 | Subspace Learing; Matrix Factorization; Deep Learning; Transfer Learning; Clustering; Multi-view Data | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-00734-8 | | isbn_ebook | 978-3-030-00734-8Series ISSN 1610-3947 Series E-ISSN 2197-8441 | | issn_series | 1610-3947 | | copyright | Springer Nature Switzerland AG 2019 |
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