kDIFFNet - Adding Extra Knowledge in Scalable Learning of Sparse Differential Gaussian Graphical Models

Tool kDIFFNet: Adding Extra Knowledge in Scalable Learning of Sparse Differential Gaussian Graphical Models

Paper: BioArxiv & PDF

Abstract

We focus on integrating different types of extra knowledge (other than the observed samples) for estimating the sparse structure change between two p-dimensional Gaussian Graphical Models (i.e. differential GGMs). Previous differential GGM estimators either fail to include additional knowledge or cannot scale up to a high-dimensional (large p) situation. This paper proposes a novel method KDiffNet that incorporates Additional Knowledge in identifying Differential Networks via an Elementary Estimator. We design a novel hybrid norm as a superposition of two structured norms guided by the extra edge information and the additional node group knowledge. KDiffNet is solved through a fast parallel proximal algorithm, enabling it to work in large-scale settings. KDiffNet can incorporate various combinations of existing knowledge without re-designing the optimization. Through rigorous statistical analysis we show that, while considering more evidence, KDiffNet achieves the same convergence rate as the state-of-the-art. Empirically on multiple synthetic datasets and one real-world fMRI brain data, KDiffNet significantly outperforms the cutting edge baselines concerning the prediction performance, while achieving the same level of time cost or less.

Citations

@conference{arsh19kdiffNet,
  Author = {Sekhon, Arshdeep and Wang, Beilun and Qi, Yanjun},
  Title = {Adding Extra Knowledge in Scalable Learning of
Sparse Differential Gaussian Graphical Models},
  Year = {2019}}
}

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