A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models

by   Beilun Wang, et al.

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(Kp^3) to O(Kp^2) and reduces the memory requirement from O(Kp^2) to O(K). (3) We theoretically prove that FASJEM achieves a consistent estimation with a convergence rate of O((Kp)/n_tot). On several synthetic and four real-world datasets, FASJEM shows significant improvements over baselines on accuracy, computational complexity and memory costs.



There are no comments yet.


page 1

page 2

page 3

page 4


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

We focus on the problem of estimating the change in the dependency struc...

A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models

We consider the problem of including additional knowledge in estimating ...

A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models

Identifying context-specific entity networks from aggregated data is an ...

Large-Scale Optimization Algorithms for Sparse Conditional Gaussian Graphical Models

This paper addresses the problem of scalable optimization for L1-regular...

Joint Gaussian Graphical Model Estimation: A Survey

Graphs from complex systems often share a partial underlying structure a...

Partially Linear Additive Gaussian Graphical Models

We propose a partially linear additive Gaussian graphical model (PLA-GGM...

AdaPtive Noisy Data Augmentation (PANDA) for Simultaneous Construction Multiple Graph Models

We extend the data augmentation technique (PANDA) by Li et al. (2018) fo...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.