Randomized subspace learning approach for high dimensional low rank plus sparse matrix decomposition

In this paper, a randomized algorithm for high dimensional low rank plus sparse matrix decomposition is proposed. Existing decomposition methods are not scalable to big data since they rely on using the whole data to extract the low-rank/sparse components, and are based on an optimization problem whose dimensionality is equal to the dimension of the given… (More)