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dc.contributor.authorXue, Xiaowei
dc.contributor.authorNie, Feiping
dc.contributor.authorWang, Sen
dc.contributor.authorChang, Xiaojun
dc.contributor.authorStantic, Bela
dc.contributor.authorYao, Min
dc.contributor.editorSatinder Singh, Shaul Markovitch
dc.date.accessioned2017-12-04T03:28:33Z
dc.date.available2017-12-04T03:28:33Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10072/355072
dc.description.abstractLearning multiple heterogeneous features from different data sources is challenging. One research topic is how to exploit and utilize the correlations among various features across multiple views with the aim of improving the performance of learning tasks, such as classification. In this paper, we propose a new multi-view feature learning algorithm that simultaneously analyzes features from different views. Compared to most of the existing subspace learning methods that only focus on exploiting a shared latent subspace, our algorithm not only learns individual information in each view but also captures feature correlations among multiple views by learning a shared component. By assuming that such a component is shared by all views, we simultaneously exploit the shared component and individual information of each view in a batch mode. Since the objective function is non-smooth and difficult to solve, we propose an efficient iterative algorithm for optimization with guaranteed convergence. Extensive experiments are conducted on several benchmark datasets. The results demonstrate that our proposed algorithm performs better than all the compared multi-view learning algorithms.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherAssociation for the Advancement of Artificial Intelligence (AAAI)
dc.publisher.placeUnited States
dc.publisher.urihttps://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14439
dc.relation.ispartofconferencename31st AAAI Conference on Artificial Intelligence
dc.relation.ispartofconferencetitleTHIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
dc.relation.ispartofdatefrom2017-02-04
dc.relation.ispartofdateto2017-02-09
dc.relation.ispartoflocationSan Francisco, CA
dc.relation.ispartofpagefrom2810
dc.relation.ispartofpagefrom7 pages
dc.relation.ispartofpageto2816
dc.relation.ispartofpageto7 pages
dc.subject.fieldofresearchArtificial intelligence not elsewhere classified
dc.subject.fieldofresearchcode460299
dc.titleMulti-view correlated feature learning by uncovering shared component
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.hasfulltextNo Full Text
gro.griffith.authorStantic, Bela


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    Contains papers delivered by Griffith authors at national and international conferences.

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