LEADER 03432nam 22005895 450 001 9910736023303321 005 20230727113449.0 010 $a3-031-33560-0 024 7 $a10.1007/978-3-031-33560-0 035 $a(MiAaPQ)EBC30668291 035 $a(Au-PeEL)EBL30668291 035 $a(DE-He213)978-3-031-33560-0 035 $a(PPN)272251798 035 $a(CKB)27867633400041 035 $a(EXLCZ)9927867633400041 100 $a20230727d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMulti-aspect Learning $eMethods and Applications /$fby Richi Nayak, Khanh Luong 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (191 pages) 225 1 $aIntelligent Systems Reference Library,$x1868-4408 ;$v242 311 08$aPrint version: Nayak, Richi Multi-Aspect Learning Cham : Springer International Publishing AG,c2023 9783031335594 327 $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. 330 $aThis book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clustering and their applications in various domains. This is the first book to systematically review multi-aspect data learning, incorporating a range of concepts and applications. Additionally, it is the first to comprehensively investigate manifold learning for dimensionality reduction in multi-view data learning. The book presents the latest advances in matrix factorization, subspace clustering, spectral clustering and deep learning methods, with a particular emphasis on the challenges and characteristics of multi-aspect data. Each chapter includes a thorough discussion of state-of-the-art of multi-aspect data learning methods and important research gaps. The book provides readers with the necessary foundational knowledge to apply these methods to new domains and applications, as well as inspire new research in this emerging field. 410 0$aIntelligent Systems Reference Library,$x1868-4408 ;$v242 606 $aEngineering?Data processing 606 $aComputational intelligence 606 $aMachine learning 606 $aData Engineering 606 $aComputational Intelligence 606 $aMachine Learning 615 0$aEngineering?Data processing. 615 0$aComputational intelligence. 615 0$aMachine learning. 615 14$aData Engineering. 615 24$aComputational Intelligence. 615 24$aMachine Learning. 676 $a620.00285 700 $aNayak$b Richi$01361180 701 $aLuong$b Khanh$01380287 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910736023303321 996 $aMulti-Aspect Learning$93421568 997 $aUNINA