Neural Comput - Direct learning of sparse changes in Markov networks by density ratio estimation.

Tópicos

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Resumo

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.

Resumo Limpo

propos new method detect chang markov network structur two set sampl instead naiv fit two markov network model separ two data set figur differ direct learn network structur chang estim ratio markov network model densityratio formul natur allow us introduc sparsiti network structur chang high contribut enhanc interpret furthermor comput normal term critic bottleneck naiv approach can remark mitig also give dual formul optim problem reduc comput cost largescal markov network experi demonstr use method

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