Effective and Efficient Multi-View Imputation With Optimal Transport

The multi-view data with incomplete information hinder effective data analysis. Existing multi-view imputation methods, which learn the mapping between a complete view and a completely missing view, are not able to deal with the typical multi-view data with missing feature information. In this paper, we propose a unified generative imputation model named {\sf UGit} UGit with optimal transport theory to simultaneously impute the missing features/values of all incomplete views. This imputation is conditional on all the observed values from the multi-view data. {\sf UGit} UGit consists of two modules, i.e., a unified multi-view generator (UMG) and a masking energy discriminator (MED). To effectively and efficiently impute missing features across all views, the generator UMG employs a unified autoencoder in conjunction with the cross-view attention mechanism to learn the data distribution from all observed multi-view data. The discriminator MED leverages a novel masking energy divergence function to make {\sf UGit} UGit differentiable for imputation accuracy enhancement. Extensive experiments on several real-world multi-view data sets demonstrate that, {\sf UGit} UGit speeds up the model training by 4.28x with more than 41% accuracy gain on average, compared to the state-of-the-art approaches.