Consensus Ranking with Signed Permutations

Signed permutations (also known as the hyperoctahedral group) are used in modeling genome rearrangements. The algorithmic problems they raise are computationally demanding when not NP-hard. This paper presents a tractable algorithm for learning consensus ranking between signed permutations under the inversion distance. This can be extended to estimate a natural class of exponential models over the group of signed permutations. We investigate experimentally the eciency of our algorithm for modeling data generated by random reversals.

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