When Is Network Lasso Accurate?
Research output: Contribution to journal › Article
|Journal||Frontiers in Applied Mathematics and Statistics|
|State||Published - 2018|
|MoE publication type||A1 Journal article-refereed|
efficient and scalable implementations of the network Lasso are available, only little is known about the conditions on the underlying network structure which
ensure network Lasso to be accurate. By leveraging concepts of compressed sensing, we address this gap and derive precise conditions on the underlying network topology and sampling set which guarantee the network Lasso for a particular loss function to deliver an accurate estimate of the entire underlying graph signal. We also quantify the error incurred by network Lasso in terms of two constants which reflect the connectivity of the sampled nodes.
- big data, compressed sensing, convex optimization, total variation regularization, machine learning, complex networks, network flow