| 書目名稱 | Feature and Dimensionality Reduction for Clustering with Deep Learning |
| 編輯 | Frederic Ros,Rabia Riad |
| 視頻video | http://file.papertrans.cn/342/341568/341568.mp4 |
| 概述 | Presents a synthesis of recent influencing techniques and "tricks" participating in advances in deep clustering.Highlights works by “family” to provide a more suitable starting point to develop a full |
| 叢書名稱 | Unsupervised and Semi-Supervised Learning |
| 圖書封面 |  |
| 描述 | .This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and "tricks" participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by “family” to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers.. |
| 出版日期 | Book 2024 |
| 關(guān)鍵詞 | Contrastive learning; Deep clustering; Self-supervision; Pseudo-labeling; Deep feature selection; Pretext |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-48743-9 |
| isbn_softcover | 978-3-031-48745-3 |
| isbn_ebook | 978-3-031-48743-9Series ISSN 2522-848X Series E-ISSN 2522-8498 |
| issn_series | 2522-848X |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
|Archiver|手機(jī)版|小黑屋|
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