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
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