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https://hdl.handle.net/2440/103667
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Type: | Journal article |
Title: | Unsupervised metric fusion over multiview data by graph random walk-based cross-view diffusion |
Author: | Wang, Y. Zhang, W. Wu, L. Lin, X. Zhao, X. |
Citation: | IEEE Transactions on Neural Networks and Learning Systems, 2017; 28(1):57-70 |
Publisher: | Institute of Electrical and Electronics Engineers |
Issue Date: | 2017 |
ISSN: | 2162-237X 2162-2388 |
Statement of Responsibility: | Yang Wang, Wenjie Zhang, Lin Wu, Xuemin Lin and Xiang Zhao |
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. |
Keywords: | Cross-view fusion, graph random walk, metric fusion, multiview data |
Rights: | © 2015 IEEE. |
DOI: | 10.1109/TNNLS.2015.2498149 |
Grant ID: | http://purl.org/au-research/grants/arc/DP120104168 |
Published version: | http://dx.doi.org/10.1109/tnnls.2015.2498149 |
Appears in Collections: | Aurora harvest 7 Computer Science publications |
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