For statistical modeling wherein the data regime is unfavorable in terms of dimensionality relative to the sample size, finding hidden sparsity in the relationship structure between variables can be critical in formulating an accurate statistical model. The so-called “ \(\ell _0\) norm”, which counts the number of non-zero components in a vector, is a strong reliable mechanism of enforcing sparsity when incorporated into an optimization problem for minimizing the fit of a given model to a set of observations. However, in big data settings wherein noisy estimates of the gradient must be evaluated out of computational necessity, the literature is scant on methods that reliably converge. In this paper, we present an approach towards solving expectation objective optimization problems with cardinality constraints. We prove convergence of the underlying stochastic process and demonstrate the performance on two Machine Learning problems.
