A Hybrid Imputation Method Based on Denoising Restricted Boltzmann Machine

Data imputation is an important issue in data processing and analysis which has serious impact on the results of data mining and learning. Most of the existing algorithms are either utilizing whole data sets for imputation or only considering the correlation among records. Aiming at these problems, the article proposes a hybrid method to fill incomplete data. In order to reduce interference and computation, denoising restricted Boltzmann machine model is developed for robust feature extraction from incomplete data and clustering. Then, the article proposes partial-distance and co-occurrence matrix strategies to measure correlation between records and attributes, respectively. Finally, quantifiable correlation is converted to weights for imputation. Compared with different algorithms, the experimental results confirm the effectiveness and efficiency of the proposed method in data imputation.