Structure Learning of Partitioned Markov Networks

TL;DR

This paper introduces a new method for learning the structure of Markov Networks between two groups of variables using a partitioned ratio, enabling efficient and accurate inter-group structure recovery from joint observations.

Contribution

The paper proposes a novel partitioned ratio concept and a convex optimization approach for direct learning of inter-group Markov Network structures, with theoretical guarantees.

Findings

Method outperforms existing approaches in ROC curve evaluations.

Successfully applied to US congress bipartisanship analysis.

Effective in DNA and time-series alignment tasks.

Abstract

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.