title: Direct learning of sparse changes in Markov networks by density ratio estimation.
abstract: 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 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, 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.
authors.affiliation: Tokyo Institute of Technology, Meguro, Tokyo 152-8552, Japan song@sg.cs.titech.ac.jp.
authors.forename: Song
authors.initials: S
authors.lastname: Liu
authors.forename: John A
authors.initials: JA
authors.lastname: Quinn
authors.forename: Michael U
authors.initials: MU
authors.lastname: Gutmann
authors.forename: Taiji
authors.initials: T
authors.lastname: Suzuki
authors.forename: Masashi
authors.initials: M
authors.lastname: Sugiyama
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D007858:Learning / Q000502:physiology*; D008390:Markov Chains; D016571:Neural Networks, Computer