Feature and Dimensionality Reduction for Clustering with Deep Learning Ros, Frederic. creator author. aut http://id.loc.gov/vocabulary/relators/aut Riad, Rabia. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng
electronic
electronic resource
access XI, 268 p. 1 illus. online resource. 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. 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. by Frederic Ros, Rabia Riad. Telecommunication Computational intelligence Data mining Pattern recognition systems Communications Engineering, Networks Computational Intelligence Data Mining and Knowledge Discovery Automated Pattern Recognition TK5101-5105.9 Springer Nature eBook Unsupervised and Semi-Supervised Learning 9783031487439 https://doi.org/10.1007/978-3-031-48743-9 https://doi.org/10.1007/978-3-031-48743-9 231221 20251030110858.0 978-3-031-48743-9