Understanding Circulant Permutation to Extend the CMinHash Estimator

Abstract:CMinHash promises a new direction for designing MinHash algorithms with reduced variance and storage space via circulant permutations. However, the original variance analysis is difficult to extend to other MinHash estimators. We present a new approach that gives explicit and easily comparable approximate variance expressions, and use it to analyze the Minner estimator. Numerical experiments show that our variance expression matches the empirical mean square error (MSE) of estimates. Our work thereby demonstrates how to apply circulant permutation to other MinHash estimators and compute their approximate variance.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2610.18551 [stat.ML]

(or arXiv:2610.18551v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2610.18551 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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