Li, S, Lopez Garcia, M orcid.org/0000-0003-3833-8595, Lawrence, ND et al. (1 more author) (2022) Two-way Sparse Network Inference for Count Data. In: Proceedings of Machine Learning Research. 25th International Conference on Artificial Intelligence and Statistics, 28-30 Mar 2022 MLResearchPress , pp. 10924-10938.
Abstract
Classically, statistical datasets have a larger number of data points than features (n > p). The standard model of classical statistics caters for the case where data points are considered conditionally independent given the parameters. However, for n ≈ p or p > n such models are poorly determined. Kalaitzis et al. (2013) introduced the Bigraphical Lasso, an estimator for sparse precision matrices based on the Cartesian product of graphs. Unfortunately, the original Bigraphical Lasso algorithm is not applicable in case of large p and n due to memory requirements. We exploit eigenvalue decomposition of the Cartesian product graph to present a more efficient version of the algorithm which reduces memory requirements from O(n 2p 2 ) to O(n 2 +p 2 ). Many datasets in different application fields, such as biology, medicine and social science, come with count data, for which Gaussian based models are not applicable. Our multiway network inference approach can be used for discrete data. Our methodology accounts for the dependencies across both instances and features, reduces the computational complexity for high dimensional data and enables to deal with both discrete and continuous data. Numerical studies on both synthetic and real datasets are presented to showcase the performance of our method.
Metadata
Authors/Creators: |
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Copyright, Publisher and Additional Information: | Copyright 2022 by the author(s). Reproduced in accordance with the publisher's self-archiving policy. |
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) > Applied Mathematics (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Apr 2022 13:00 |
Last Modified: | 31 May 2023 15:28 |
Status: | Published |
Publisher: | MLResearchPress |
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