03426nam a22004935i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001800153072001600171072002300187072001500210100007600225245013000301250001800431264006900449300004200518336002600560337002600586338003600612347002400648490005800672505033200730520126901062650002302331650003202354650001702386650003302403650004202436650003202478650004102510650003502551700007402586710003402660773002602694776003602720776003602756776003602792830005802828856004602886978-3-031-48743-9DE-He21320251030110858.0cr nn 008mamaa231221s2024    sz |    s    |||| 0|eng d  a97830314874397 a10.1007/978-3-031-48743-92doi 4aTK5101-5105.9 7aTJK2bicssc 7aTEC0410002bisacsh 7aTJK2thema1 aRos, Frederic.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aFeature and Dimensionality Reduction for Clustering with Deep Learningh[electronic resource] /cby Frederic Ros, Rabia Riad.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXI, 268 p. 1 illus.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aUnsupervised and Semi-Supervised Learning,x2522-84980 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.  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. 0aTelecommunication. 0aComputational intelligence. 0aData mining. 0aPattern recognition systems.14aCommunications Engineering, Networks.24aComputational Intelligence.24aData Mining and Knowledge Discovery.24aAutomated Pattern Recognition.1 aRiad, Rabia.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303148742208iPrinted edition:z978303148744608iPrinted edition:z9783031487453 0aUnsupervised and Semi-Supervised Learning,x2522-849840uhttps://doi.org/10.1007/978-3-031-48743-9