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A new imputation method for incomplete binary data

By Munevver Mine Subasi, Ersoy Subasi, Martin Anthony and Peter L. Hammer

Abstract

In data analysis problems where the data are represented by vectors of real numbers, it is often the case that some of the data-points will have "missing values", meaning that one or more of the entries of the vector that describes the data-point is not observed. In this paper, we propose a new approach to the imputation of missing binary values. The technique we introduce employs a "similarity measure" introduced by Anthony and Hammer (2006) [1]. We compare experimentally the performance of our technique with ones based on the usual Hamming distance measure and multiple imputation

Topics: QA Mathematics
Publisher: Elsevier
Year: 2011
DOI identifier: 10.1016/j.dam.2011.01.024
OAI identifier: oai:eprints.lse.ac.uk:37105
Provided by: LSE Research Online
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