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.
Bibliographical noteFunding Information:
This research is partially supported by the US National Science Foundation (NSF) under grants # DMS-1622483, # DMS-1737918, # OAC-1939916 and #DMR-1939956.
- High dimensional
- Principal components
- Record linkage