Scalable Bayesian nonparametric dictionary learning

Sarper Sertoglu, John W. Paisley

Abstract: We derive a stochastic EM algorithm for scalable dictionary learning with the beta-Bernoulli process, a Bayesian nonpara-metric prior that learns the dictionary size in addition to the sparse coding of each signal. The core EM algorithm provides a new way for doing inference in nonparametric dictionary learning models and has a close similarity to other sparse coding methods such as K-SVD. Our stochastic extension for handling large data sets is closely related to stochastic variational inference, with the stochastic update for one parameter exactly that found using SVI. We show our algorithm compares well with K-SVD and total variation minimization on a denoising problem using several images.