Database alignment with Gaussian features

Osman Emre Dai; Daniel Cullina; Negar Kiyavash

Database alignment with Gaussian features

Osman Emre Dai, Daniel Cullina, Negar Kiyavash

Electrical Engineering

Research output: Contribution to conference › Paper › peer-review

20 Scopus citations

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.

Original language	English (US)
State	Published - 2020
Event	22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019 - Naha, Japan Duration: Apr 16 2019 → Apr 18 2019

Conference

Conference	22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019
Country/Territory	Japan
City	Naha
Period	4/16/19 → 4/18/19

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Statistics and Probability

Cite this

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title = "Database alignment with Gaussian features",

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.",

author = "Dai, {Osman Emre} and Daniel Cullina and Negar Kiyavash",

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TY - CONF

T1 - Database alignment with Gaussian features

AU - Dai, Osman Emre

AU - Cullina, Daniel

AU - Kiyavash, Negar

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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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M3 - Paper

AN - SCOPUS:85084990650

T2 - 22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019

Y2 - 16 April 2019 through 18 April 2019

ER -

Database alignment with Gaussian features

Abstract

Conference

All Science Journal Classification (ASJC) codes

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