Dealing with missing data using attention and latent space regularization

• URL: http://arxiv.org/abs/2211.07059v1
• Date: Mon, 14 Nov 2022 01:05:28 GMT
• Title: Dealing with missing data using attention and latent space regularization
• Authors: Jahan C. Penny-Dimri, Christoph Bergmeir, Julian Smith
• Abstract summary: We develop a theoretical framework for training and inference using only observed variables. We construct models with latent space representations that regularize against the potential bias introduced by missing data. We show that our proposed method overcomes the weaknesses of imputation methods and outperforms the current state-of-the-art.
• Score: 2.610470075814367
• License: http://creativecommons.org/licenses/by/4.0/
• 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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