Clustering with label constrained Dirichlet process mixture model

Abstract

In this paper, we present a constrained Dirichlet process mixture model with labels as side information. Specifically, the labeled information is incorporated through the use of a product partition prior to give clusters of instances with similar labels a higher prior preference. The proposed formulation is further extended to handle multiple side information. The empirical results on several benchmark datasets show that our method can consistently improve its clustering performance as more labeled data become available. Even in the presence of noisy labels, the proposed method rarely performs worse than its unsupervised counterpart. The effectiveness of the proposed method is also demonstrated through an application of magnetic resonance imaging for identifying major brain tissues.