Unified constraint propagation on multi-view data

Abstract

This paper presents a unified framework for intra-view and inter-view constraint propagation on multi-view data. Pairwise constraint propagation has been studied extensively, where each pair-wise constraint is defined over a pair of data points from a single view. In contrast, very little attention has been paid to inter-view constraint propagation, which is more challenging since each pair-wise constraint is now defined over a pair of data points from different views. Although both interview and inter-view constraint propagation are crucial for multi-view tasks, most previous methods can not handle them simultaneously. To address this challenging issue, we propose to decompose these two types of constraint propagation into semi-supervised learning sub-problems so that they can be uniformly solved based on the traditional label propagation techniques. To further integrate them into a unified framework, we utilize the results of intra-view constraint propagation to adjust the similarity matrix of each view and then perform inter-view constraint propagation with the adjusted similarity matrices. The experimental results in cross-view retrieval have shown the superior performance of our unified constraint propagation. © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.