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2020 | 21 | nr 4 Special Issue | 123--143
Tytuł artykułu

High Dimensional, Robust, Unsupervised Record Linkage

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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. (original abstract)
Rocznik
Tom
21
Strony
123--143
Opis fizyczny
Twórcy
  • University of Minnesota, USA
  • University of Minnesota, USA
Bibliografia
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171624030

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