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Abstract:
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We develop a novel probabilistic model for graph matchings and present a 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 matchings. Furthermore, our framework generalizes naturally to K-partite hypergraph matching 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 novel quadripartite matching problem arising from the field of computational forestry as well as image matching problem. We demonstrate our novel scalable SMC method, streaming SMC, that we use for sampling from the space of graph matchings.
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