03768nam a22005775i 4500 978-3-031-48743-9 DE-He213 20251030110858.0 cr nn 008mamaa 231221s2024 sz | s |||| 0|eng d 9783031487439 978-3-031-48743-9 10.1007/978-3-031-48743-9 doi TK5101-5105.9 TJK bicssc TEC041000 bisacsh TJK thema Ros, Frederic. author. aut http://id.loc.gov/vocabulary/relators/aut 68418 Feature and Dimensionality Reduction for Clustering with Deep Learning [electronic resource] / by Frederic Ros, Rabia Riad. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XI, 268 p. 1 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Unsupervised and Semi-Supervised Learning, 2522-8498 Introduction -- Representation Learning in high dimension -- Review of Feature selection and clustering approaches -- Towards deep learning -- Deep learning architectures for feature extraction and selection -- Unsupervised Deep Feature selection techniques -- Deep Clustering Techniques -- Issues and Challenges -- Conclusion. 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. 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 understanding of the domain; Includes recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks. Telecommunication. Computational intelligence. 64052 Data mining. 6941 Pattern recognition systems. 65547 Communications Engineering, Networks. Computational Intelligence. 64052 Data Mining and Knowledge Discovery. Automated Pattern Recognition. 65551 Riad, Rabia. author. aut http://id.loc.gov/vocabulary/relators/aut 68419 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031487422 Printed edition: 9783031487446 Printed edition: 9783031487453 Unsupervised and Semi-Supervised Learning, 2522-8498 68420 https://doi.org/10.1007/978-3-031-48743-9 ZDB-2-ENG ZDB-2-SXE 56873 56873