000			02783nam a22003135i 4500
001			401870
003			DE-He213
005			20240514120028.0
007			cr nn 008mamaa
008			231221s2024 sz | o |||| 0|eng d
020			_a9783031487439
024	7		_a10.1007/978-3-031-48743-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5101-5105.9 _b2024 EB
100	1		_aRos, Frederic. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	0	0	_aFeature and Dimensionality Reduction for Clustering with Deep Learning _cby Frederic Ros, Rabia Riad
250			_a1st ed. 2024.
264		1	_aCham _bSpringer International Publishing _c2024
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aUnsupervised and Semi-Supervised Learning _x2522-8498
505	0		_aIntroduction -- 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.
520			_aThis 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.
942			_2lcc _cLE
988			_aSpringer_Engineering_2024
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-48743-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
999			_c401870 _d401870