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8 February 2015 Missing value imputation: with application to handwriting data
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Proceedings Volume 9402, Document Recognition and Retrieval XXII; 94020P (2015)
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States
Missing values make pattern analysis difficult, particularly with limited available data. In longitudinal research, missing values accumulate, thereby aggravating the problem. Here we consider how to deal with temporal data with missing values in handwriting analysis. In the task of studying development of individuality of handwriting, we encountered the fact that feature values are missing for several individuals at several time instances. Six algorithms, i.e., random imputation, mean imputation, most likely independent value imputation, and three methods based on Bayesian network (static Bayesian network, parameter EM, and structural EM), are compared with children's handwriting data. We evaluate the accuracy and robustness of the algorithms under different ratios of missing data and missing values, and useful conclusions are given. Specifically, static Bayesian network is used for our data which contain around 5% missing data to provide adequate accuracy and low computational cost.
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Zhen Xu and Sargur N. Srihari "Missing value imputation: with application to handwriting data", Proc. SPIE 9402, Document Recognition and Retrieval XXII, 94020P (8 February 2015);

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