Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
This method addresses the need for interpretable and efficient change detection in Markov networks, which is incremental as it builds on existing density ratio estimation techniques.
The paper tackles the problem of detecting changes in Markov network structure between two datasets by directly learning sparse changes through density ratio estimation, resulting in enhanced interpretability and reduced computational cost compared to naive separate fitting methods.
We propose a new method for detecting changes in Markov network structure between two sets of samples. Instead of naively fitting two Markov network models separately to the two data sets and figuring out their difference, we \emph{directly} learn the network structure change by estimating the ratio of Markov network models. This density-ratio formulation naturally allows us to introduce sparsity in the network structure change, which highly contributes to enhancing interpretability. Furthermore, computation of the normalization term, which is a critical bottleneck of the naive approach, can be remarkably mitigated. We also give the dual formulation of the optimization problem, which further reduces the computation cost for large-scale Markov networks. Through experiments, we demonstrate the usefulness of our method.