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008 231221s2024 sz | s |||| 0|eng d
020 _a9783031487439
_9978-3-031-48743-9
050 4 _aTK5101-5105.9
072 7 _aTJK
_2bicssc
072 7 _aTEC041000
_2bisacsh
072 7 _aTJK
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082 0 4 _a621.382
_223
100 1 _aRos, Frederic.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
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.
541 _fUABC ;
_cPerpetuidad
650 0 _aTelecommunication.
650 0 _aComputational intelligence.
650 0 _aData mining.
650 0 _aPattern recognition systems.
650 1 4 _aCommunications Engineering, Networks.
650 2 4 _aComputational Intelligence.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aAutomated Pattern Recognition.
700 1 _aRiad, Rabia.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
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
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-48743-9
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cLIBRO_ELEC
999 _c273817
_d273816