Feature and Dimensionality Reduction for Clustering with Deep Learning / / by Frederic Ros, Rabia Riad

• Record Nr.: UNINA9910799479503321
• Autore: Ros Frederic
• Pubbl/distr/stampa: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
• ISBN: 9783031487439; 3031487435; 9783031487422; 3031487427
• Edizione: [1st ed. 2024.]
• Descrizione fisica: 1 online resource (xi, 268 pages) : illustrations
• Collana: Unsupervised and Semi-Supervised Learning, , 2522-8498
• Disciplina: 621.382
• Soggetti: Telecommunication; Computational intelligence; Data mining; Pattern recognition systems; Communications Engineering, Networks; Computational Intelligence; Data Mining and Knowledge Discovery; Automated Pattern Recognition
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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.
• Sommario/riassunto: 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.