000			03768nam a22005775i 4500
001			978-3-031-48743-9
003			DE-He213
005			20251030110858.0
007			cr nn 008mamaa
008			231221s2024 sz | s |||| 0|eng d
020			_a9783031487439 _9978-3-031-48743-9
024	7		_a10.1007/978-3-031-48743-9 _2doi
050		4	_aTK5101-5105.9
072		7	_aTJK _2bicssc
072		7	_aTEC041000 _2bisacsh
072		7	_aTJK _2thema
100	1		_aRos, Frederic. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _968418
245	1	0	_aFeature and Dimensionality Reduction for Clustering with Deep Learning _h[electronic resource] / _cby Frederic Ros, Rabia Riad.
250			_a1st ed. 2024.
264		1	_aCham : _bSpringer Nature Switzerland : _bImprint: Springer, _c2024.
300			_aXI, 268 p. 1 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_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.
650		0	_aTelecommunication.
650		0	_aComputational intelligence. _964052
650		0	_aData mining. _96941
650		0	_aPattern recognition systems. _965547
650	1	4	_aCommunications Engineering, Networks.
650	2	4	_aComputational Intelligence. _964052
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aAutomated Pattern Recognition. _965551
700	1		_aRiad, Rabia. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _968419
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783031487422
776	0	8	_iPrinted edition: _z9783031487446
776	0	8	_iPrinted edition: _z9783031487453
830		0	_aUnsupervised and Semi-Supervised Learning, _x2522-8498 _968420
856	4	0	_uhttps://doi.org/10.1007/978-3-031-48743-9
912			_aZDB-2-ENG
912			_aZDB-2-SXE
999			_c56873 _d56873