2016
Active learning on weighted graphs using adaptive and non-adaptive approaches
ICASSP 2016accepted
This paper studies graph-based active learning, where the goal is to reconstruct a binary signal defined on the nodes of a weighted graph, by sampling it on a small subset of the nodes. A new sampling algorithm is proposed, which sequentially selects the graph nodes to be sampled, based on an aggres…