Database Alignment with Gaussian Features
This work addresses database alignment for users in data integration or matching tasks, but it appears incremental as it builds on existing methods for Gaussian features.
The paper tackles the problem of aligning databases with Gaussian features by analyzing two algorithms: complete alignment via MAP estimation and partial alignment via thresholding of log likelihood ratios. It derives conditions on mutual information that determine when these algorithms are guaranteed to succeed or fail.
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.