Andrew, B., Westhead, D.R. and Cutillo, L. orcid.org/0000-0002-2205-0338 (Accepted: 2024) GmGM: a fast multi-axis Gaussian graphical model. In: Proceedings of AISTATS 2024. The 27th International Conference on Artificial Intelligence and Statistics, 02-04 May 2024, València, Spain. Proceedings of Machine Learning Research
Abstract
This paper introduces the Gaussian multiGraphical Model, a model to construct sparse graph representations of matrix- and tensorvariate data. We generalize prior work in this area by simultaneously learning this representation across several tensors that share axes, which is necessary to allow the analysis of multimodal datasets such as those encountered in multi-omics. Our algorithm uses only a single eigendecomposition per axis, achieving an order of magnitude speedup over prior work in the ungeneralized case. This allows the use of our methodology on large multi-modal datasets such as singlecell multi-omics data, which was challenging with previous approaches. We validate our model on synthetic data and five real-world datasets.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Jan 2024 10:46 |
Last Modified: | 29 Aug 2024 12:31 |
Published Version: | https://proceedings.mlr.press/v238/ |
Status: | Published |
Publisher: | Proceedings of Machine Learning Research |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:208283 |