Fused multiple graphical lasso

Sen Yang, Zhaosong Lu, Xiaotong Shen, Peter Wonka, Jieping Ye

Research output: Contribution to journalArticle

32 Scopus citations

Abstract

In this paper, we consider the problem of estimating multiple graphical models simultaneously using the fused lasso penalty, which encourages adjacent graphs to share similar structures. A motivating example is the analysis of brain networks of Alzheimer's disease using neuroimaging data. Specifically, we may wish to estimate a brain network for the normal controls (NC), a brain network for the patients with mild cognitive impairment (MCI), and a brain network for Alzheimer's patients (AD). We expect the two brain networks for NC and MCI to share common structures but not to be identical to each other; similarly for the two brain networks for MCI and AD. The proposed formulation can be solved using a second-order method. Our key technical contribution is to establish the necessary and sufficient condition for the graphs to be decomposable. Based on this key property, a simple screening rule is presented, which decomposes the large graphs into small subgraphs and allows an efficient estimation of multiple independent (small) subgraphs, dramatically reducing the computational cost. We perform experiments on both synthetic and real data; our results demonstrate the effectiveness and efficiency of the proposed approach.

Original languageEnglish (US)
Pages (from-to)916-943
Number of pages28
JournalSIAM Journal on Optimization
Volume25
Issue number2
DOIs
StatePublished - Jan 1 2015

Keywords

  • Fused multiple graphical lasso
  • Screening
  • Second-order method

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