Database Alignment with Gaussian Features

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Osman E. Dai, Daniel Cullina, Negar Kiyavash ;
Proceedings of Machine Learning Research, PMLR 89:3225-3233, 2019.

Abstract

We consider the problem of aligning a pair of databases with jointly Gaussian features. We consider two algorithms, complete database alignment via MAP estimation among all possible database alignments, and partial alignment via a thresholding approach of log likelihood ratios. We derive conditions on mutual information between feature pairs, identifying the regimes where the algorithms are guaranteed to perform reliably and those where they cannot be expected to succeed.

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