Filling in the Blanks - Krimp Minimisation for Missing Data

Many data sets are incomplete. For correct analysis of such data, one can either use algorithms that are designed to handle missing data or use imputation. Imputation has the benefit that it allows for any type of data analysis. Obviously, this can only lead to proper conclusions if the provided data completion is both highly accurate and maintains all statistics of the original data. In this paper, we present three data completion methods that are built on the MDL-based {\sc Krimp} algorithm. Here, we also follow the MDL principle, i.e. the completed database that can be compressed best, is the best completion because it adheres best to the patterns in the data. By using local patterns, as opposed to a global model, Krimp captures the structure of the data in detail. Experiments show that both in terms of accuracy and expected differences of any marginal, better data reconstructions are provided than the state of the art, Structural EM.