Simultaneous Sparse Approximation and Common Component Extraction using Fast Distributed Compressive Sensing

Simultaneous sparse approximation is a generalization of the standard sparse approximation, for simultaneously representing a set of signals using a common sparsity model. Generalizing the compressive sensing concept to the simultaneous sparse approximation yields distributed compressive sensing (DCS). DCS finds the sparse representation of multiple correlated signals using the common + innovation signal model. However, DCS is not efficient for joint recovery of a large number of signals since it requires large memory and computational time. In this paper, we propose a new hierarchical algorithm to implement the jointly sparse recovery framework of DCS more efficiently. The proposed algorithm is applied to video background extraction problem, where the background corresponds to the common sparse activity across frames. Keywords—Functional Connectivity Network, Network State, Summarization, Compressive Sensing, Distributed Compressive Sensing,Hierarchal Distributed Compressive Sensing.