A polynomial-time approximation algorithm for the permanent of a matrix with non-negative entries
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Bipartite graph partitioning and data clustering
Proceedings of the tenth international conference on Information and knowledge management
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Lazy inference on object identities in wireless sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Fast approximation of the permanent for very dense problems
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Parimutuel Betting on Permutations
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
Fourier Theoretic Probabilistic Inference over Permutations
The Journal of Machine Learning Research
A distributed algorithm for managing multi-target identities in wireless ad-hoc sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
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We compare two recently proposed approaches for representing probability distributions over the space of permutations in the context of multi-target tracking. We show that these two representations, the Fourier approximation and the information form approximation can both be viewed as low dimensional projections of a true distribution, but with respect to different metrics. We identify the strengths and weaknesses of each approximation, and propose an algorithm for converting between the two forms, allowing for a hybrid approach that draws on the strengths of both representations. We show experimental evidence that there are situations where hybrid algorithms are favorable.