03952nam 22006855 450 991079947950332120250808083513.09783031487422(print)3031487427(print)9783031487439303148743510.1007/978-3-031-48743-9(CKB)29449770100041(DE-He213)978-3-031-48743-9(MiAaPQ)EBC31051271(Au-PeEL)EBL31051271(OCoLC)1416156406(EXLCZ)992944977010004120231221d2024 u| 0engur|n#|||a|||atxtrdacontentcrdamediacrrdacarrierFeature and Dimensionality Reduction for Clustering with Deep Learning /by Frederic Ros, Rabia Riad1st ed. 2024.Cham :Springer Nature Switzerland :Imprint: Springer,2024.1 online resource (xi, 268 pages) illustrationsUnsupervised and Semi-Supervised Learning,2522-84989783031487422 Includes bibliographical references and index.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.Unsupervised and Semi-Supervised Learning,2522-8498TelecommunicationComputational intelligenceData miningPattern recognition systemsCommunications Engineering, NetworksComputational IntelligenceData Mining and Knowledge DiscoveryAutomated Pattern RecognitionTelecommunication.Computational intelligence.Data mining.Pattern recognition systems.Communications Engineering, Networks.Computational Intelligence.Data Mining and Knowledge Discovery.Automated Pattern Recognition.621.382Ros Fredericauthttp://id.loc.gov/vocabulary/relators/aut1585609Riad Rabiaauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910799479503321Feature and Dimensionality Reduction for Clustering with Deep Learning3870940UNINA