TY  - JOUR
PY  - 2015//
TI  - Unsupervised metric fusion over multiview data by graph random walk-based cross-view diffusion
JO  - IEEE transactions on neural networks and learning systems
A1  - Wang, Yang
A1  - Zhang, Wenjie
A1  - Wu, Lin
A1  - Lin, Xuemin
A1  - Zhao, Xiang
SP  - 57
EP  - 70
VL  - 28
IS  - 1
N2  - 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>
LA  - en
SN  - 2162-237X
UR  - http://dx.doi.org/10.1109/TNNLS.2015.2498149
ID  - ref1
ER  -