DIFFEE to identify Sparse Changes in High-Dimensional Gaussian Graphical Model Structure

Tool DIFFEE: Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure

Paper: @Arxiv | To Appear at 2018 AISTAT

Poster @ NIPS 2017 workshop for Advances in Modeling and Learning Interactions from Complex Data.

Abstract

We focus on the problem of estimating the change in the dependency structures of two p-dimensional Gaussian Graphical models (GGMs). Previous studies for sparse change estimation in GGMs involve expensive and difficult non-smooth optimization. We propose a novel method, DIFFEE for estimating DIFFerential networks via an Elementary Estimator under a high-dimensional situation. DIFFEE is solved through a faster and closed form solution that enables it to work in large-scale settings. We conduct a rigorous statistical analysis showing that surprisingly DIFFEE achieves the same asymptotic convergence rates as the state-of-the-art estimators that are much more difficult to compute. Our experimental results on multiple synthetic datasets and one real-world data about brain connectivity show strong performance improvements over baselines, as well as significant computational benefits.

Citations

@article{DBLP:journals/corr/abs-1710-11223,
  author    = {Beilun Wang and
               Arshdeep Sekhon and
               Yanjun Qi},
  title     = {Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian
               Graphical Model Structure},
  journal   = {CoRR},
  volume    = {abs/1710.11223},
  year      = {2017},
  url       = {http://arxiv.org/abs/1710.11223},
  archivePrefix = {arXiv},
  eprint    = {1710.11223},
  timestamp = {Thu, 02 Nov 2017 14:25:36 +0100},
  biburl    = {http://dblp.org/rec/bib/journals/corr/abs-1710-11223},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

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