Structure Learning of Partitioned Markov Networks
This addresses a specific bottleneck in probabilistic graphical modeling for applications like political analysis and bioinformatics, though it appears incremental as it builds on existing MN structure learning methods.
The paper tackles the problem of learning the structure of Markov Networks between two groups of variables by introducing a partitioned ratio concept and a convex optimization method, achieving theoretical guarantees and experimental validation with ROC curves.
We learn the structure of a Markov Network between two groups of random variables from joint observations. Since modelling and learning the full MN structure may be hard, learning the links between two groups directly may be a preferable option. We introduce a novel concept called the \emph{partitioned ratio} whose factorization directly associates with the Markovian properties of random variables across two groups. A simple one-shot convex optimization procedure is proposed for learning the \emph{sparse} factorizations of the partitioned ratio and it is theoretically guaranteed to recover the correct inter-group structure under mild conditions. The performance of the proposed method is experimentally compared with the state of the art MN structure learning methods using ROC curves. Real applications on analyzing bipartisanship in US congress and pairwise DNA/time-series alignments are also reported.