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The performance of EM in learning mixtures of product distributions oftendepends on the initialization. This can be problematic in crowdsourcing andother applications, e.g. when a small number of 'experts' are diluted by alarge number of noisy, unreliable participants. We develop a new EM algorithmthat is driven by these experts. In a manner that differs from otherapproaches, we start from a single mixture class. The algorithm then developsthe set of 'experts' in a stagewise fashion based on a mutual informationcriterion. At each stage EM operates on this subset of the players, effectivelyregularizing the E rather than the M step. Experiments show that stagewise EMoutperforms other initialization techniques for crowdsourcing and neurosciencesapplications, and can guide a full EM to results comparable to those obtainedknowing the exact distribution.

Stagewise Learning for Sparse Clustering of Discretely-Valued Data