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[null,"1.","Record Nr.",null,"UNINA9910799479503321"]
[null,null,"Autore",null,"Ros Frederic"]
[null,null,"Titolo",null,"Feature and Dimensionality Reduction for Clustering with Deep Learning / / by Frederic Ros, Rabia Riad"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"9783031487439"]
[{"$nulls":4},"3031487435"]
[{"$nulls":4},"9783031487422"]
[{"$nulls":4},"3031487427"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (xi, 268 pages) : illustrations"]
[null,null,"Collana",null,"Unsupervised and Semi-Supervised Learning, , 2522-8498"]
[null,null,"Disciplina",null,"621.382"]
[null,null,"Soggetti",null,"Telecommunication"]
[{"$nulls":4},"Computational intelligence"]
[{"$nulls":4},"Data mining"]
[{"$nulls":4},"Pattern recognition systems"]
[{"$nulls":4},"Communications Engineering, Networks"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Automated Pattern Recognition"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"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."]
[null,null,null,"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 "]
[{"$nulls":4},"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."]