antGLasso: An Efficient Tensor Graphical Lasso Algorithm
Abstract
The class of bigraphical lasso algorithms (and, more broadly, 'tensor'-graphical lasso algorithms) has been used to estimate dependency structures within matrix and tensor data. However, all current methods to do so take prohibitively long on modestly sized datasets. We present a novel tensor-graphical lasso algorithm that directly estimates the dependency structure, unlike its iterative predecessors. This provides a speedup of multiple orders of magnitude, allowing this class of algorithms to be used on large, real-world datasets.
Cite
Text
Andrew et al. "antGLasso: An Efficient Tensor Graphical Lasso Algorithm." NeurIPS 2022 Workshops: GLFrontiers, 2022.Markdown
[Andrew et al. "antGLasso: An Efficient Tensor Graphical Lasso Algorithm." NeurIPS 2022 Workshops: GLFrontiers, 2022.](https://mlanthology.org/neuripsw/2022/andrew2022neuripsw-antglasso/)BibTeX
@inproceedings{andrew2022neuripsw-antglasso,
title = {{antGLasso: An Efficient Tensor Graphical Lasso Algorithm}},
author = {Andrew, Bailey and Westhead, David and Cutillo, Luisa},
booktitle = {NeurIPS 2022 Workshops: GLFrontiers},
year = {2022},
url = {https://mlanthology.org/neuripsw/2022/andrew2022neuripsw-antglasso/}
}