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 structure among a system of entities has garnered 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 anomalies. We propose Clustered Fused Graphical Lasso (CFGL), a method using precomputed 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 structure among a system of entities has garnered 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 anomalies. We propose Clustered Fused Graphical Lasso (CFGL), a method using precomputed 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 structure among a system of entities has garnered 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 anomalies. We propose Clustered Fused Graphical Lasso (CFGL), a method using precomputed 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.

Related Material