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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.
| Autorius: | Rabia Riad, Frederic Ros, |
| Leidėjas: | Springer Nature Switzerland |
| Išleidimo metai: | 2024 |
| Knygos puslapių skaičius: | 280 |
| ISBN-10: | 3031487427 |
| ISBN-13: | 9783031487422 |
| Formatas: | 241 x 160 x 21 mm. Knyga kietu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Feature and Dimensionality Reduction for Clustering with Deep Learning“
