Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/103667
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dc.contributor.author | Wang, Y. | - |
dc.contributor.author | Zhang, W. | - |
dc.contributor.author | Wu, L. | - |
dc.contributor.author | Lin, X. | - |
dc.contributor.author | Zhao, X. | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | IEEE Transactions on Neural Networks and Learning Systems, 2017; 28(1):57-70 | - |
dc.identifier.issn | 2162-237X | - |
dc.identifier.issn | 2162-2388 | - |
dc.identifier.uri | http://hdl.handle.net/2440/103667 | - |
dc.description.abstract | Learning 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. | - |
dc.description.statementofresponsibility | Yang Wang, Wenjie Zhang, Lin Wu, Xuemin Lin and Xiang Zhao | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.rights | © 2015 IEEE. | - |
dc.source.uri | http://dx.doi.org/10.1109/tnnls.2015.2498149 | - |
dc.subject | Cross-view fusion, graph random walk, metric fusion, multiview data | - |
dc.title | Unsupervised metric fusion over multiview data by graph random walk-based cross-view diffusion | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1109/TNNLS.2015.2498149 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP120104168 | - |
pubs.publication-status | Published | - |
Appears in Collections: | Aurora harvest 7 Computer Science publications |
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