ONLINE NONNEGATIVE MATRIX FACTORIZATION WITH OUTLIERS

We propose an optimization framework for performing online Non-negative Matrix Factorization (NMF) in the presence of outliers, based on l(1) regularization and stochastic approximation. Due to the online nature of the algorithm, the proposed method has extremely low computational and storage complexity and is thus particularly applicable in this age of BigData. Furthermore, our algorithm shows promising performance in dealing with outliers, which previous online NMF algorithms fail to cope with. Convergence analysis shows the dictionary learned by our algorithm converges to that learned by its batch counterpart almost surely, as data size tends to infinity. We show numerically on a range of face datasets that our algorithm is superior to the state-of-the-art NMF algorithms in terms of running time, basis representations and reconstruction of original images. We also observe that our algorithm performs well even when the density of outliers reaches 40%. We provide explanations behind this seemingly surprising result.