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 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. 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 UGit differentiable for imputation accuracy enhancement. Extensive experiments on several real-world multi-view data sets demonstrate that, 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.