PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A new imputation method for incomplete binary data
Munevver Mine Subasi, Ersoy Subasi, Martin Anthony and Peter L. Hammer
Discrete Applied Mathematics Volume 159, Number 10, pp. 1040-1047, 2011.

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). We compare experimentally the performance of our technique with ones based on the usual Hamming distance measure and multiple imputation.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:8585
Deposited By:Martin Anthony
Deposited On:12 February 2012