Estimation of mixture models using Co-EM

We study estimation of mixture models for problems in which multiple views of the instances are available. Examples of this setting include clustering web pages or research papers that have intrinsic (text) and extrinsic (references) attributes. Our optimization criterion quantifies the likelihood and the consensus among models in the individual views; maximizing this consensus minimizes a bound on the risk of assigning an instance to an incorrect mixture component. We derive an algorithm that maximizes this criterion. Empirically, we observe that the resulting clustering method incurs a lower cluster entropy than regular EM for web pages, research papers, and many text collections.