Variational Autoencoding with Conditional Iterative Sampling for Missing Data Imputation

Shenfen Kuang (), Jie Song, Shangjiu Wang and Huafeng Zhu
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Shenfen Kuang: School of Mathematics and Statistics, Shaoguan University, Shaoguan 512005, China
Jie Song: School of Mathematics and Statistics, Shaoguan University, Shaoguan 512005, China
Shangjiu Wang: School of Mathematics and Statistics, Shaoguan University, Shaoguan 512005, China
Huafeng Zhu: School of Mathematics and Statistics, Shaoguan University, Shaoguan 512005, China

Mathematics, 2024, vol. 12, issue 20, 1-16

Abstract: Variational autoencoders (VAEs) are popular for their robust nonlinear representation capabilities and have recently achieved notable advancements in the problem of missing data imputation. However, existing imputation methods often exhibit instability due to the inherent randomness in the sampling process, leading to either underestimation or overfitting, particularly when handling complex missing data types such as images. To address this challenge, we introduce a conditional iterative sampling imputation method. Initially, we employ an importance-weighted beta variational autoencoder to learn the conditional distribution from the observed data. Subsequently, leveraging the importance-weighted resampling strategy, samples are drawn iteratively from the conditional distribution to compute the conditional expectation of the missing data. The proposed method has been experimentally evaluated using classical generative datasets and compared with various well-known imputation methods to validate its effectiveness.

Keywords: missing data; variational autoencoder; conditional distribution; importance-weighted resampling (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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