Matrix Completion for Cross-view Pairwise Constraint Propagation

As pairwise constraints are usually easier to access than label information, pairwise constraint propagation attracts more and more attention in semi-supervised learning. Most existing pairwise constraint propagation methods are based on canonical graph propagation model, which heavily depends on the edge weights in the graph and cannot preserve local and global consistency simultaneously. In order to address this drawback, we cast cross-view pairwise constraint propagation into a problem of low rank matrix completion and propose a Matrix Completion method for cross-view Pairwise Constraint Propagation(MCPCP). With low rank requirement and graph regularization, our MCPCP can preserve local and global consistency simultaneously. We develop an algorithm based on alternating direction method of multipliers(ADMM) to solve the optimization problem. Finally, the effectiveness of MCPCP is demonstrated in cross-view multimedia retrieval.