Clustering by transitive propagation

Vijay Kumar; Dan Levy

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arXiv · 1506.03072

Clustering by transitive propagation

Vijay Kumar·Dan Levy

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Abstract

We present a global optimization algorithm for clustering data given the ratio of likelihoods that each pair of data points is in the same cluster or in different clusters. To define a clustering solution in terms of pairwise relationships, a necessary and sufficient condition is that belonging to the same cluster satisfies transitivity. We define a global objective function based on pairwise likelihood ratios and a transitivity constraint over all triples, assigning an equal prior probability to all clustering solutions. We maximize the objective function by implementing max-sum message passing on the corresponding factor graph to arrive at an O(N^3) algorithm. Lastly, we demonstrate an application inspired by mutational sequencing for decoding random binary words transmitted through a noisy channel.

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cs.LG cond-mat.stat-mech stat.ML

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BibTeX RIS

Vijay Kumar, Dan Levy. 2015-06-09. Clustering by transitive propagation. https://arxiv.org/abs/1506.03072

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