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arXiv · 2211.07059

Dealing with missing data using attention and latent space regularization

Jahan C. Penny-Dimri·Christoph Bergmeir·Julian Smith
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Abstract

Most practical data science problems encounter missing data. A wide variety of solutions exist, each with strengths and weaknesses that depend upon the missingness-generating process. Here we develop a theoretical framework for training and inference using only observed variables enabling modeling of incomplete datasets without imputation. Using an information and measure-theoretic argument we construct models with latent space representations that regularize against the potential bias introduced by missing data. The theoretical properties of this approach are demonstrated empirically using a synthetic dataset. The performance of this approach is tested on 11 benchmarking datasets with missingness and 18 datasets corrupted across three missingness patterns with comparison against a state-of-the-art model and industry-standard imputation. We show that our proposed method overcomes the weaknesses of imputation methods and outperforms the current state-of-the-art.

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Jahan C. Penny-Dimri, Christoph Bergmeir, Julian Smith. 2022-11-14. Dealing with missing data using attention and latent space regularization. https://arxiv.org/abs/2211.07059

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