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|Title:||Unsupervised metric fusion over multiview data by graph random walk-based cross-view diffusion|
|Citation:||IEEE Transactions on Neural Networks and Learning Systems, 2017; 28(1):57-70|
|Publisher:||Institute of Electrical and Electronics Engineers|
|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.|
|Appears in Collections:||Computer Science publications|
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