Online Low-Rank plus Sparse Structure Learning for Dynamic Network Tracking

Recent developments in information technology have enabled us to collect and analyze high dimensional and higher order data such as tensors. High dimensional data usually lies in a lower dimensional subspace and identifying this low-dimensional structure is important in many signal and information processing applications. Traditional subspace estimation approaches have been limited to vector-type data and cannot effectively deal with these high order datasets. Moreover, most of the existing methods are batch algorithms which can't handle streaming data. In this paper, we propose a new tensor subspace tracking approach to identify changes in dynamic networks. The proposed approach recursively estimates low-rank subspace of higher order data and decomposes it into low-rank and sparse components. The proposed approach is evaluated on both simulated and real dynamic networks.