Nonparametric prior for adaptive sparsity
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:629-636, 2010.
For high-dimensional problems various parametric priors have been proposed to promote sparse solutions. While parametric priors has shown considerable success they are not very robust in adapting to varying degrees of sparsity. In this work we propose a discrete mixture prior which is partially nonparametric. The right structure for the prior and the amount of sparsity is estimated directly from the data. Our experiments show that the proposed prior adapts to sparsity much better than its parametric counterparts. We apply the proposed method to classification of high dimensional microarray datasets.