Source: Columbia University
Motivated by applications in compression and distributed transform coding, the authors propose a new gossip algorithm called Selective Gossip to efficiently compute sparse approximations of network data. They consider running parallel gossip algorithms on the elements of a vector of transform coefficients. Unlike classical randomized gossip, communication between adjacent nodes is data driven and only performed if deemed to significantly improve the estimate of the signal vector. In particular nodes adaptively estimate and focus on using communication resources to compute significant coefficients (above a pre-defined threshold in magnitude).