03461nam 22005535 450 991033784910332120200705060005.03-030-00734-010.1007/978-3-030-00734-8(CKB)4100000007204847(MiAaPQ)EBC5611894(DE-He213)978-3-030-00734-8(PPN)23296436X(EXLCZ)99410000000720484720181206d2019 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierLearning Representation for Multi-View Data Analysis Models and Applications /by Zhengming Ding, Handong Zhao, Yun Fu1st ed. 2019.Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (272 pages)Advanced Information and Knowledge Processing,1610-39473-030-00733-2 Introduction -- 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. .This 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.Advanced Information and Knowledge Processing,1610-3947Data miningArtificial intelligencePattern recognitionData Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XData mining.Artificial intelligence.Pattern recognition.Data Mining and Knowledge Discovery.Artificial Intelligence.Pattern Recognition.006.31Ding Zhengmingauthttp://id.loc.gov/vocabulary/relators/aut1064836Zhao Handongauthttp://id.loc.gov/vocabulary/relators/autFu Yunauthttp://id.loc.gov/vocabulary/relators/autBOOK9910337849103321Learning Representation for Multi-View Data Analysis2541143UNINA