000 03782nam a22005775i 4500
001 978-3-031-33560-0
003 DE-He213
005 20240207153524.0
007 cr nn 008mamaa
008 230727s2023 sz | s |||| 0|eng d
020 _a9783031335600
_9978-3-031-33560-0
050 4 _aTA345-345.5
072 7 _aUN
_2bicssc
072 7 _aCOM018000
_2bisacsh
072 7 _aUN
_2thema
082 0 4 _a620.00285
_223
100 1 _aNayak, Richi.
_eauthor.
_0(orcid)0000-0002-9954-0159
_1https://orcid.org/0000-0002-9954-0159
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aMulti-aspect Learning
_h[electronic resource] :
_bMethods and Applications /
_cby Richi Nayak, Khanh Luong.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2023.
300 _aVIII, 184 p. 71 illus., 70 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aIntelligent Systems Reference Library,
_x1868-4408 ;
_v242
500 _aAcceso multiusuario
505 0 _a1 Multi-Aspect Data Learning: Overview, Challenges and Approaches -- 2 Non-negative Matrix Factorization-Based Multi-Aspect Data Clustering -- 3 NMF and Manifold Learning for Multi-Aspect Data -- 4 Subspace Learning for Multi-Aspect Data -- 5 Spectral Clustering on Multi-Aspect Data -- 6 Learning Consensus and Complementary Information for Multi-Aspect Data Clustering -- 7 Deep Learning-Based Methods for Multi-Aspect Data Clustering.
520 _aThis book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clustering and their applications in various domains. This is the first book to systematically review multi-aspect data learning, incorporating a range of concepts and applications. Additionally, it is the first to comprehensively investigate manifold learning for dimensionality reduction in multi-view data learning. The book presents the latest advances in matrix factorization, subspace clustering, spectral clustering and deep learning methods, with a particular emphasis on the challenges and characteristics of multi-aspect data. Each chapter includes a thorough discussion of state-of-the-art of multi-aspect data learning methods and important research gaps. The book provides readers with the necessary foundational knowledge to apply these methods to new domains and applications, as well as inspire new research in this emerging field.
541 _fUABC ;
_cPerpetuidad
650 0 _aEngineering
_xData processing.
650 0 _aComputational intelligence.
650 0 _aMachine learning.
650 1 4 _aData Engineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aMachine Learning.
700 1 _aLuong, Khanh.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031335594
776 0 8 _iPrinted edition:
_z9783031335617
776 0 8 _iPrinted edition:
_z9783031335624
830 0 _aIntelligent Systems Reference Library,
_x1868-4408 ;
_v242
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-33560-0
912 _aZDB-2-INR
912 _aZDB-2-SXIT
942 _cLIBRO_ELEC
999 _c261020
_d261019