000 03457nam a22005775i 4500
001 978-3-030-00734-8
003 DE-He213
005 20240423125150.0
007 cr nn 008mamaa
008 181206s2019 sz | s |||| 0|eng d
020 _a9783030007348
_9978-3-030-00734-8
024 7 _a10.1007/978-3-030-00734-8
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
072 7 _aUNF
_2thema
072 7 _aUYQE
_2thema
082 0 4 _a006.312
_223
100 1 _aDing, Zhengming.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aLearning Representation for Multi-View Data Analysis
_h[electronic resource] :
_bModels and Applications /
_cby Zhengming Ding, Handong Zhao, Yun Fu.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aX, 268 p. 76 illus., 69 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 _aAdvanced Information and Knowledge Processing,
_x2197-8441
505 0 _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. .
520 _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.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 0 _aPattern recognition systems.
650 1 4 _aData Mining and Knowledge Discovery.
650 2 4 _aArtificial Intelligence.
650 2 4 _aAutomated Pattern Recognition.
700 1 _aZhao, Handong.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aFu, Yun.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030007331
776 0 8 _iPrinted edition:
_z9783030007355
830 0 _aAdvanced Information and Knowledge Processing,
_x2197-8441
856 4 0 _uhttps://doi.org/10.1007/978-3-030-00734-8
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c174970
_d174970