Estimating conditional density of missing values using deep Gaussian mixture modelDownload PDF

Published: 06 Jul 2020, Last Modified: 05 May 2023ICML Artemiss 2020Readers: Everyone
TL;DR: Deep Gaussian Mixture Model For Missing Data
Keywords: missing data, density estimation, imputation, Gaussian mixture model, neural networks, conditional density
Abstract: We consider the problem of estimating the conditional probability distribution of missing values given the observed ones. We propose an approach, which combines the flexibility of deep neural networks with the simplicity of Gaussian mixture models (GMMs). Given an incomplete data point, our neural network returns the parameters of Gaussian distribution (in the form of Factor Analyzers model) representing the corresponding conditional density. We experimentally verify that our model provides better log-likelihood than conditional GMM trained in a typical way. Moreover, imputation obtained by replacing missing values using the mean vector of our model looks visually plausible.
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