Reliable Learning of Bernoulli Mixture Models
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Reliable Learning of Bernoulli Mixture Models
In this paper, we have derived a set of sufficient conditions for reliable clustering of data produced by Bernoulli Mixture Models (BMM), when the number of clusters is unknown. A BMM refers to a random binary vector whose components are independent Bernoulli trials with clusterspecific frequencies. The problem of clustering BMM data arises in many realworld applications, most notably in population genetics where researchers aim at inferring the population structure from multilocus genotype data. Our findings stipulate a minimum dataset size and a minimum number of Bernoulli trials (or genotyped loci) per sample, such that the existence of a clustering algorithm with a sufficient accuracy is guaranteed. Moreover, the mathematical intuitions and tools behind our work can help researchers in designing more effective and theoreticallyplausible heuristic methods for similar problems.
Reliable Learning of Bernoulli Mixture Models
by Amir Najafi, Abolfazl Motahari, Hamid R. Rabiee
https://arxiv.org/pdf/1710.02101v1.pdf
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