Please use this identifier to cite or link to this item: http://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.
RMID: 0030041231
DOI: 10.1109/TNNLS.2015.2498149
Grant ID: http://purl.org/au-research/grants/arc/DP120104168
Appears in Collections:Computer Science publications

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