| 書目名稱 | Multi-aspect Learning | | 副標題 | Methods and Applicat | | 編輯 | Richi Nayak,Khanh Luong | | 視頻video | http://file.papertrans.cn/641/640046/640046.mp4 | | 概述 | Provides a comprehensive review and in-depth discussion on the multi-aspect data learning.Focuses on the state-of-the-art approaches.A comprehensive review of methods dealing with the challenges of mu | | 叢書名稱 | Intelligent Systems Reference Library | | 圖書封面 |  | | 描述 | .This book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clustering and their applications in various domains. This is the first book to systematically review multi-aspect data learning, incorporating a range of concepts and applications. Additionally, it is the first to comprehensively investigate manifold learning for dimensionality reduction in multi-view data learning. The book presents the latest advances in matrix factorization, subspace clustering, spectral clustering and deep learning methods, with a particular emphasis on the challenges and characteristics of multi-aspect data. Each chapter includes a thorough discussion of state-of-the-art of multi-aspect data learning methods and important research gaps. The book provides readers with the necessary foundational knowledge to apply these methods to new domains and applications, as well as inspire new research in this emerging field.. | | 出版日期 | Book 2023 | | 關鍵詞 | Multi-aspect Data Learning; Multi-view Data Learning; Non-negative Matrix Factorization; Subspace Learn | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-33560-0 | | isbn_softcover | 978-3-031-33562-4 | | isbn_ebook | 978-3-031-33560-0Series ISSN 1868-4394 Series E-ISSN 1868-4408 | | issn_series | 1868-4394 | | copyright | Springer Nature Switzerland AG 2023 |
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