Sequential Graph Matching with Sequential Monte Carlo
Abstract
We develop a novel probabilistic model for graph matchings and develop practical inference methods for supervised and unsupervised learning of the parameters of this model. The framework we develop admits joint inference on the parameters and the matching. Furthermore, our framework generalizes naturally to $K$-partite hypergraph matchings or set packing problems. The sequential formulation of the graph matching process naturally leads to sequential Monte Carlo algorithms which can be combined with various parameter inference methods. We apply our method to image matching problems, document ranking, and our own novel quadripartite matching problem arising from the field of computational forestry.
Cite
Text
Jun et al. "Sequential Graph Matching with Sequential Monte Carlo." International Conference on Artificial Intelligence and Statistics, 2017.Markdown
[Jun et al. "Sequential Graph Matching with Sequential Monte Carlo." International Conference on Artificial Intelligence and Statistics, 2017.](https://mlanthology.org/aistats/2017/jun2017aistats-sequential/)BibTeX
@inproceedings{jun2017aistats-sequential,
title = {{Sequential Graph Matching with Sequential Monte Carlo}},
author = {Jun, Seong-Hwan and Wong, Samuel W. K. and Zidek, James V. and Bouchard-Côté, Alexandre},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2017},
pages = {1075-1084},
url = {https://mlanthology.org/aistats/2017/jun2017aistats-sequential/}
}