Latent Dirichlet Allocation (LDA) currently requires that the priors’ concentration parameters be > 1.0. It should support values > 0.0, which should encourage sparser topics (phi) and document-topic distributions (theta).
For EM, this will require adding a projection to the M-step, as in: Vorontsov and Potapenko. "Tutorial on Probabilistic Topic Modeling : Additive Regularization for Stochastic Matrix Factorization." 2014.