Estimating Conditional Density of Missing Values Using Deep Gaussian Mixture Model
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.
Cite
Text
Przewięźlikowski et al. "Estimating Conditional Density of Missing Values Using Deep Gaussian Mixture Model." ICML 2020 Workshops: Artemiss, 2020.Markdown
[Przewięźlikowski et al. "Estimating Conditional Density of Missing Values Using Deep Gaussian Mixture Model." ICML 2020 Workshops: Artemiss, 2020.](https://mlanthology.org/icmlw/2020/przewiezlikowski2020icmlw-estimating/)BibTeX
@inproceedings{przewiezlikowski2020icmlw-estimating,
title = {{Estimating Conditional Density of Missing Values Using Deep Gaussian Mixture Model}},
author = {Przewięźlikowski, Marcin and Śmieja, Marek and Struski, Łukasz},
booktitle = {ICML 2020 Workshops: Artemiss},
year = {2020},
url = {https://mlanthology.org/icmlw/2020/przewiezlikowski2020icmlw-estimating/}
}