Recent SVD-free matrix factorization formulations have enabled rank minimization for systems with millions of rows and columns, paving the way for matrix completion for extremely high dimensional data. In this talk, we discuss a robust matrix completion algorithm that uses factorized matrix decomposition with a pre-specified rank to compute the low rank component. We then adopt a block coordinate descent approach using spectral projected gradient steps to alternate between the solution of the low rank component and the sparse component all the while traversing the updated Pareto curve of each subproblem. We illustrate the performance of our algorithm for video background subtraction.