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010   $z9783031487422$b(print)
010   $z3031487427$b(print)
010   $a9783031487439
010   $a3031487435
024 7 $a10.1007/978-3-031-48743-9
035   $a(CKB)29449770100041
035   $a(DE-He213)978-3-031-48743-9
035   $a(MiAaPQ)EBC31051271
035   $a(Au-PeEL)EBL31051271
035   $a(OCoLC)1416156406
035   $a(EXLCZ)9929449770100041
100   $a20231221d2024      u|         0
101 0 $aeng
135   $aur|n#|||a|||a
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aFeature and Dimensionality Reduction for Clustering with Deep Learning /$fby Frederic Ros, Rabia Riad
205   $a1st ed. 2024.
210  1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024.
215   $a1 online resource (xi, 268 pages) $cillustrations
225 1 $aUnsupervised and Semi-Supervised Learning,$x2522-8498
311 08$a9783031487422 
320   $aIncludes bibliographical references and index.
327   $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.
330   $aThis book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and "tricks" participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by ?family? to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers. Presents a synthesis of recent influencing techniques and "tricks" participating in advances in deep clustering; Highlights works by ?family? to provide a more suitable starting point to develop a full understanding of the domain; Includes recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks.
410  0$aUnsupervised and Semi-Supervised Learning,$x2522-8498
606   $aTelecommunication
606   $aComputational intelligence
606   $aData mining
606   $aPattern recognition systems
606   $aCommunications Engineering, Networks
606   $aComputational Intelligence
606   $aData Mining and Knowledge Discovery
606   $aAutomated Pattern Recognition
615  0$aTelecommunication.
615  0$aComputational intelligence.
615  0$aData mining.
615  0$aPattern recognition systems.
615 14$aCommunications Engineering, Networks.
615 24$aComputational Intelligence.
615 24$aData Mining and Knowledge Discovery.
615 24$aAutomated Pattern Recognition.
676   $a621.382
700   $aRos$b Frederic$4aut$4http://id.loc.gov/vocabulary/relators/aut$01585609
702   $aRiad$b Rabia$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910799479503321
996   $aFeature and Dimensionality Reduction for Clustering with Deep Learning$93870940
997   $aUNINA