Title: Feature Selection Facilitates Learning Mixtures of Discrete Product Distributions,
Abstract: Feature selection can facilitate the learning of mixtures of discrete random
variables as they arise, e.g. in crowdsourcing tasks. Intuitively, not all
workers are equally reliable but, if the less reliable ones could be
eliminated, then learning should be more robust. By analogy with Gaussian
mixture models, we seek a low-order statistical approach, and here introduce an
algorithm based on the (pairwise) mutual information. This induces an order
over workers that is well structured for the `one coin' model. More generally,
it is justified by a goodness-of-fit measure and is validated empirically.
Improvement in real data sets can be substantial.