A concave regularization technique for sparse mixture models Johan Ugander School of Operations Research and Information Engineering Center for Applied Mathematics Cornell University

Latent variable mixture models are a powerful tool for exploring the structure in large datasets. A common challenge for interpreting such models is a desire to impose sparsity, the natural assumption that each data point only contains few latent features.