FASJEM (Index of Posts):


FASJEM R package is released!

R package: fasjem

install.packages("fasjem")
library(fasjem)
demo(fasjem)

Package Manual

Paper: @AISTAT17 | @Arxiv

GitHub

Poster

Abstract

Estimating multiple sparse Gaussian Graphical Models (sGGMs) jointly for many related tasks (large K) under a high-dimensional (large p) situation is an important task. Most previous studies for the joint estimation of multiple sGGMs rely on penalized log-likelihood estimators that involve expensive and difficult non-smooth optimizations. We propose a novel approach, FASJEM for fast and scalable joint structure-estimation of multiple sGGMs at a large scale. As the first study of joint sGGM using the M-estimator framework, our work has three major contributions: (1) We solve FASJEM through an entry-wise manner which is parallelizable. (2) We choose a proximal algorithm to optimize FASJEM. This improves the computational efficiency from O(Kp3 ) to O(Kp2 ) and reduces the memory requirement from O(Kp2 ) to O(K). (3) We theoretically prove that FASJEM achieves a consistent estimation with a convergence rate of O(log(Kp)/ntot). On several synthetic and four real-world datasets, FASJEM shows significant improvements over baselines on accuracy, computational complexity and memory costs.

JEM

JEM2

JEMmore

Citations

@inproceedings{wang2017fast,
  title={A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models},
  author={Wang, Beilun and Gao, Ji and Qi, Yanjun},
  booktitle={Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR:, 2017.},
  volume={54},
  pages={1168--1177},
  year={2017}
}

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