Semi-supervised Clustering with Pairwise Constraints: A Discriminative Approach
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:299-306, 2007.
We consider the semi-supervised clustering problem where we know (with varying degree of certainty) that some sample pairs are (or are not) in the same class. Unlike previous efforts in adapting clustering algorithms to incorporate those pairwise relations, our work is based on a discriminative model. We generalize the standard Gaussian process classifier (GPC) to express our classification preference. To use the samples not involved in pairwise relations, we employ the graph kernels (covariance matrix) based on the entire data set. Experiments on a variety of data sets show that our algorithm significantly outperforms several state-of-the-art methods.