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Citation Information : Statistics in Transition New Series. Volume 21, Issue 4, Pages 123-143, DOI: https://doi.org/10.21307/stattrans-2020-034
License : (CC BY-NC-ND 4.0)
Received Date : 31-January-2020 / Accepted: 30-June-2020 / Published Online: 15-September-2020
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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