  The performance of EM in learning mixtures of product distributions often
depends on the initialization. This can be problematic in crowdsourcing and
other applications, e.g. when a small number of 'experts' are diluted by a
large number of noisy, unreliable participants. We develop a new EM algorithm
that is driven by these experts. In a manner that differs from other
approaches, we start from a single mixture class. The algorithm then develops
the set of 'experts' in a stagewise fashion based on a mutual information
criterion. At each stage EM operates on this subset of the players, effectively
regularizing the E rather than the M step. Experiments show that stagewise EM
outperforms other initialization techniques for crowdsourcing and neurosciences
applications, and can guide a full EM to results comparable to those obtained
knowing the exact distribution.
