Clustered Fused Graphical Lasso

Yizhi Zhu, Oluwasanmi Koyejo
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:486-495, 2018.

Abstract

Estimating the dynamic connectivity struc- ture among a system of entities has gar- nered much attention in recent years. While usual methods are designed to take advantage of temporal consistency to overcome noise, they conflict with the detectability of anoma- lies. We propose Clustered Fused Graphi- cal Lasso (CFGL), a method using precom- puted clustering information to improve the signal detectability as compared to typical Fused Graphical Lasso methods. We evaluate our method in both simulated and real-world datasets and conclude that, in many cases, CFGL can significantly improve the sensitivity to signals without a significant negative effect on the temporal consistency.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-zhu18a, title = {Clustered Fused Graphical Lasso}, author = {Zhu, Yizhi and Koyejo, Oluwasanmi}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {486--495}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/zhu18a/zhu18a.pdf}, url = {https://proceedings.mlr.press/r16/zhu18a.html}, abstract = {Estimating the dynamic connectivity struc- ture among a system of entities has gar- nered much attention in recent years. While usual methods are designed to take advantage of temporal consistency to overcome noise, they conflict with the detectability of anoma- lies. We propose Clustered Fused Graphi- cal Lasso (CFGL), a method using precom- puted clustering information to improve the signal detectability as compared to typical Fused Graphical Lasso methods. We evaluate our method in both simulated and real-world datasets and conclude that, in many cases, CFGL can significantly improve the sensitivity to signals without a significant negative effect on the temporal consistency.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Clustered Fused Graphical Lasso %A Yizhi Zhu %A Oluwasanmi Koyejo %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-zhu18a %I PMLR %P 486--495 %U https://proceedings.mlr.press/r16/zhu18a.html %V R16 %X Estimating the dynamic connectivity struc- ture among a system of entities has gar- nered much attention in recent years. While usual methods are designed to take advantage of temporal consistency to overcome noise, they conflict with the detectability of anoma- lies. We propose Clustered Fused Graphi- cal Lasso (CFGL), a method using precom- puted clustering information to improve the signal detectability as compared to typical Fused Graphical Lasso methods. We evaluate our method in both simulated and real-world datasets and conclude that, in many cases, CFGL can significantly improve the sensitivity to signals without a significant negative effect on the temporal consistency. %Z Reissued by PMLR on 04 October 2026.
APA
Zhu, Y. & Koyejo, O.. (2018). Clustered Fused Graphical Lasso. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:486-495 Available from https://proceedings.mlr.press/r16/zhu18a.html. Reissued by PMLR on 04 October 2026.

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