Jointly Imputing Multi-View Data with Optimal Transport
DOI:
https://doi.org/10.1609/aaai.v37i4.25599Keywords:
DMKM: Mining of Visual, Multimedia & Multimodal DataAbstract
The multi-view data with incomplete information hinder the effective data analysis. Existing multi-view imputation methods that learn the mapping between complete view and completely missing view are not able to deal with the common multi-view data with missing feature information. In this paper, we propose a generative imputation model named Git with optimal transport theory to jointly impute the missing features/values, conditional on all observed values from the multi-view data. Git consists of two modules, i.e., a multi-view joint generator (MJG) and a masking energy discriminator (MED). The generator MJG incorporates a joint autoencoder with the multiple imputation rule to learn the data distribution from all observed multi-view data. The discriminator MED leverages a new masking energy divergence function to make Git differentiable for imputation enhancement. Extensive experiments on several real-world multi-view data sets demonstrate that, Git yields over 35% accuracy gain, compared to the state-of-the-art approaches.Downloads
Published
2023-06-26
How to Cite
Wu, Y., Miao, X., Huang, X., & Yin, J. (2023). Jointly Imputing Multi-View Data with Optimal Transport. Proceedings of the AAAI Conference on Artificial Intelligence, 37(4), 4747-4755. https://doi.org/10.1609/aaai.v37i4.25599
Issue
Section
AAAI Technical Track on Data Mining and Knowledge Management