LEADER 03488nam 22005535 450 001 9910337849103321 005 20200705060005.0 010 $a3-030-00734-0 024 7 $a10.1007/978-3-030-00734-8 035 $a(CKB)4100000007204847 035 $a(MiAaPQ)EBC5611894 035 $a(DE-He213)978-3-030-00734-8 035 $a(PPN)23296436X 035 $a(EXLCZ)994100000007204847 100 $a20181206d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLearning Representation for Multi-View Data Analysis$b[electronic resource] $eModels and Applications /$fby Zhengming Ding, Handong Zhao, Yun Fu 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (272 pages) 225 1 $aAdvanced Information and Knowledge Processing,$x1610-3947 311 $a3-030-00733-2 327 $aIntroduction -- Multi-view Clustering with Complete Information -- Multi-view Clustering with Partial Information -- Multi-view Outlier Detection -- Multi-view Transformation Learning -- Zero-Shot Learning -- Missing Modality Transfer Learning -- Deep Domain Adaptation -- Deep Domain Generalization. . 330 $aThis book equips readers to handle complex multi-view data representation, centered around several major visual applications, sharing many tips and insights through a unified learning framework. This framework is able to model most existing multi-view learning and domain adaptation, enriching readers? understanding from their similarity, and differences based on data organization and problem settings, as well as the research goal. A comprehensive review exhaustively provides the key recent research on multi-view data analysis, i.e., multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. More practical challenges in multi-view data analysis are discussed including incomplete, unbalanced and large-scale multi-view learning. Learning Representation for Multi-View Data Analysis covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision. 410 0$aAdvanced Information and Knowledge Processing,$x1610-3947 606 $aData mining 606 $aArtificial intelligence 606 $aPattern recognition 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 615 0$aData mining. 615 0$aArtificial intelligence. 615 0$aPattern recognition. 615 14$aData Mining and Knowledge Discovery. 615 24$aArtificial Intelligence. 615 24$aPattern Recognition. 676 $a006.31 700 $aDing$b Zhengming$4aut$4http://id.loc.gov/vocabulary/relators/aut$01064836 702 $aZhao$b Handong$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aFu$b Yun$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910337849103321 996 $aLearning Representation for Multi-View Data Analysis$92541143 997 $aUNINA