A New Imputation Method for Incomplete Binary Data ## AbstractIn 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.
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