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Author
Bera Sabyasachi (University of Minnesota, USA), Chatterjee Snigdhansu (University of Minnesota, USA)
Title
High Dimensional, Robust, Unsupervised Record Linkage
Source
Statistics in Transition, 2020, vol. 21, nr 4 Special Issue, s. 123-143, bibliogr. s. 140-143
Keyword
Analizy głównych komponentów, Modele bayesowskie
Principal Component Analysis (PCA), Bayesian models
Note
summ.
Abstract
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)
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The Main Library of the Cracow University of Economics
The Library of Warsaw School of Economics
The Library of University of Economics in Katowice
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ISSN
1234-7655
Language
eng
URI / DOI
http://dx.doi.org/10.21307/stattrans-2020-034
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