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Activity Number:
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326
- Recent Developments in Probabilistic Record Linkage, Multiple Systems Estimation, and Entity Resolution
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Type:
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Invited
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Date/Time:
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Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Survey Research Methods Section
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Abstract #314486
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Title:
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High-Dimensional, Robust, Unsupervised Record Linkage
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Author(s):
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Ansu Chatterjee* and Sabyasachi Bera
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Companies:
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University of Minnesota and University of Minnesota
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Keywords:
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Record Linkage;
High Dimensional;
Unsupervised;
Robust
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Abstract:
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We develop a technique for record linkage on high dimensional data, where the two datasets may not have any common variable, and there may be no training set available. Our methodology is based on sparse, high dimensional principal components. Since large and high dimensional datasets are often prone to outliers and aberrant observations, we propose a technique for estimating robust, high dimensional principal components. We present theoretical results validating the robust, high dimensional principal component estimation steps, and justifying their use for record linkage. Some numeric results and remarks are also presented.
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Authors who are presenting talks have a * after their name.
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