Structure Learning of Undirected Graphical Models with Contrastive Divergence


Structure learning of Markov random fields (MRFs) is generally NP-hard (Karger & Srebro, 2001). Many structure learners and theoretical results are under the correlation decay assumption in the sense that for any two nodes $i$ and $k$, the information about node $i$ captured by node k is less than that captured by node $j$ where $j$ is the neighbor of $i$ on the shortest path between $i$ and $k$ (Netrapalli et al., 2010). In this paper, we propose to learn structure of MRFs with contrastive divergence (Hinton, 2002) and demonstrate that our structure learner can recover the structures of these correlation non-decay MRFs.

ICML 2013 Workshop on Structured Learning: Inferring Graphs from Structured and Unstructured Inputs