PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A New Imputation Method for Incomplete Binary Data
Mine Subasi, Ersoy Subasi, Martin Anthony and Peter Hammer
Discrete Applied Mathematics 2010.

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 known. In this paper, we propose a new approach to the imputation of missing binary values. The technique we introduce employs a ``similarity measure'' introduced in~\cite{AH}. We compare experimentally the performance of our technique with ones based on the usual Hamming distance measure and a more classical statistical technique.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:7733
Deposited By:Martin Anthony
Deposited On:17 March 2011