Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/103667
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dc.contributor.authorWang, Y.en
dc.contributor.authorZhang, W.en
dc.contributor.authorWu, L.en
dc.contributor.authorLin, X.en
dc.contributor.authorZhao, X.en
dc.date.issued2017en
dc.identifier.citationIEEE Transactions on Neural Networks and Learning Systems, 2017; 28(1):57-70en
dc.identifier.issn2162-237Xen
dc.identifier.issn2162-2388en
dc.identifier.urihttp://hdl.handle.net/2440/103667-
dc.description.abstractLearning an ideal metric is crucial to many tasks in computer vision. Diverse feature representations may combat this problem from different aspects; as visual data objects described by multiple features can be decomposed into multiple views, thus often provide complementary information. In this paper, we propose a cross-view fusion algorithm that leads to a similarity metric for multiview data by systematically fusing multiple similarity measures. Unlike existing paradigms, we focus on learning distance measure by exploiting a graph structure of data samples, where an input similarity matrix can be improved through a propagation of graph random walk. In particular, we construct multiple graphs with each one corresponding to an individual view, and a cross-view fusion approach based on graph random walk is presented to derive an optimal distance measure by fusing multiple metrics. Our method is scalable to a large amount of data by enforcing sparsity through an anchor graph representation. To adaptively control the effects of different views, we dynamically learn view-specific coefficients, which are leveraged into graph random walk to balance multiviews. However, such a strategy may lead to an over-smooth similarity metric where affinities between dissimilar samples may be enlarged by excessively conducting cross-view fusion. Thus, we figure out a heuristic approach to controlling the iteration number in the fusion process in order to avoid over smoothness. Extensive experiments conducted on real-world data sets validate the effectiveness and efficiency of our approach.en
dc.description.statementofresponsibilityYang Wang, Wenjie Zhang, Lin Wu, Xuemin Lin and Xiang Zhaoen
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineersen
dc.rights© 2015 IEEE.en
dc.subjectCross-view fusion, graph random walk, metric fusion, multiview dataen
dc.titleUnsupervised metric fusion over multiview data by graph random walk-based cross-view diffusionen
dc.typeJournal articleen
dc.identifier.rmid0030041231en
dc.identifier.doi10.1109/TNNLS.2015.2498149en
dc.relation.granthttp://purl.org/au-research/grants/arc/DP120104168en
dc.identifier.pubid227150-
pubs.library.collectionComputer Science publicationsen
pubs.library.teamDS10en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
Appears in Collections:Computer Science publications

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