Local similarity learning for pairwise constraint propagation

Pairwise constraint propagation studies the problem of propagating the scarce pairwise constraints across the entire dataset. Effective propagation algorithms have previously been designed based on the graph-based semi-supervised learning framework. Therefore, these previous constraint propagation methods rely critically on a good similarity measure over the data points. Improper or noisy similarity measurements may dramatically degrade the performance of the constraint propagation algorithms. In this paper, we make attempt to exploit the available pairwise constraints to learn a new set of similarities, which are consistent with the supervisory information in the pairwise constraints, before propagating these initial constraints. Our method is a local learning algorithm. More specifically, we compute the similarities at each data point through simultaneously minimizing the local reconstruction error and local constraint error. The proposed method has been tested in the constrained clustering tasks on eight real-life datasets and then shown to achieve significant improvements with respect to the state of the arts.