The Annals of Statistics

Support consistency of direct sparse-change learning in Markov networks

Song Liu, Taiji Suzuki, Raissa Relator, Jun Sese, Masashi Sugiyama, and Kenji Fukumizu

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Abstract

We study the problem of learning sparse structure changes between two Markov networks $P$ and $Q$. Rather than fitting two Markov networks separately to two sets of data and figuring out their differences, a recent work proposed to learn changes directly via estimating the ratio between two Markov network models. In this paper, we give sufficient conditions for successful change detection with respect to the sample size $n_{p},n_{q}$, the dimension of data $m$ and the number of changed edges $d$. When using an unbounded density ratio model, we prove that the true sparse changes can be consistently identified for $n_{p}=\Omega(d^{2}\log\frac{m^{2}+m}{2})$ and $n_{q}=\Omega({n_{p}^{2}})$, with an exponentially decaying upper-bound on learning error. Such sample complexity can be improved to $\min(n_{p},n_{q})=\Omega(d^{2}\log\frac{m^{2}+m}{2})$ when the boundedness of the density ratio model is assumed. Our theoretical guarantee can be applied to a wide range of discrete/continuous Markov networks.

Article information

Source
Ann. Statist. Volume 45, Number 3 (2017), 959-990.

Dates
Received: December 2015
Revised: April 2016
First available in Project Euclid: 13 June 2017

Permanent link to this document
https://projecteuclid.org/euclid.aos/1497319685

Digital Object Identifier
doi:10.1214/16-AOS1470

Zentralblatt MATH identifier
1371.62022

Subjects
Primary: 62F12: Asymptotic properties of estimators
Secondary: 68T99: None of the above, but in this section

Keywords
Markov networks change detection density ratio estimation

Citation

Liu, Song; Suzuki, Taiji; Relator, Raissa; Sese, Jun; Sugiyama, Masashi; Fukumizu, Kenji. Support consistency of direct sparse-change learning in Markov networks. Ann. Statist. 45 (2017), no. 3, 959--990. doi:10.1214/16-AOS1470. https://projecteuclid.org/euclid.aos/1497319685


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Supplemental materials

  • Supplement to “Support consistency of direct sparse-change learning in Markov networks”. Due to the page limit, we present the proofs of Lemmas 1–5, corollaries and propositions in this supplementary article. We also use this supplementary material to show some detailed experimental settings, extended simulation results and illustrations of a few concepts in the paper.