Triangulation by Continuous Embedding
Abstract
When triangulating a belief network we aim to obtain a junction tree of minimum state space. According to (Rose, 1970), searching for the optimal triangulation can be cast as a search over all the permutations of the graph's vertices. Our approach is to embed the discrete set of permutations in a convex continuous domain D. By suitably extending the cost function over D and solving the continous nonlinear optimization task we hope to obtain a good triangulation with respect to the aformentioned cost. This paper presents two ways of embedding the triangulation problem into continuous domain and shows that they perform well compared to the best known heuristic.
Cite
Text
Meila and Jordan. "Triangulation by Continuous Embedding." Neural Information Processing Systems, 1996.Markdown
[Meila and Jordan. "Triangulation by Continuous Embedding." Neural Information Processing Systems, 1996.](https://mlanthology.org/neurips/1996/meila1996neurips-triangulation/)BibTeX
@inproceedings{meila1996neurips-triangulation,
title = {{Triangulation by Continuous Embedding}},
author = {Meila, Marina and Jordan, Michael I.},
booktitle = {Neural Information Processing Systems},
year = {1996},
pages = {557-563},
url = {https://mlanthology.org/neurips/1996/meila1996neurips-triangulation/}
}