  Iterative hard thresholding (IHT) is a projected gradient descent algorithm,
known to achieve state of the art performance for a wide range of structured
estimation problems, such as sparse inference. In this work, we consider IHT as
a solution to the problem of learning sparse discrete distributions. We study
the hardness of using IHT on the space of measures. As a practical alternative,
we propose a greedy approximate projection which simultaneously captures
appropriate notions of sparsity in distributions, while satisfying the simplex
constraint, and investigate the convergence behavior of the resulting procedure
in various settings. Our results show, both in theory and practice, that IHT
can achieve state of the art results for learning sparse distributions.

Machine Learning;Machine Learning;Optimization and Control