@article{ref1,
title="Unsupervised metric fusion over multiview data by graph random walk-based cross-view diffusion",
journal="IEEE transactions on neural networks and learning systems",
year="2015",
author="Wang, Yang and Zhang, Wenjie and Wu, Lin and Lin, Xuemin and Zhao, Xiang",
volume="28",
number="1",
pages="57-70",
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.<p /> <p>Language: en</p>",
language="en",
issn="2162-237X",
doi="10.1109/TNNLS.2015.2498149",
url="http://dx.doi.org/10.1109/TNNLS.2015.2498149"
}