GAMIN: Generative Adversarial Multiple Imputation Network for Highly Missing Data

We propose a novel imputation method for highly missing data. Though most existing imputation methods focus on moderate missing rate, imputation for high missing rate over 80% is still important but challenging. As we expect that multiple imputation is indispensable for high missing rate, we propose...

Full description

Saved in:
Bibliographic Details
Main Authors: Yoon, Seongwook, Sull, Sanghoon
Format: Conference Proceeding
Language:English
Subjects:
Gallium nitride
Generative adversarial networks
Generators
Measurement
Prediction methods
Stochastic processes
Unsupervised learning
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We propose a novel imputation method for highly missing data. Though most existing imputation methods focus on moderate missing rate, imputation for high missing rate over 80% is still important but challenging. As we expect that multiple imputation is indispensable for high missing rate, we propose a generative adversarial multiple imputation network (GAMIN) based on generative adversarial network (GAN) for multiple imputation. Compared with similar imputation methods adopting GAN, our method has three novel contributions: 1)We propose a novel imputation architecture which generates candidates of imputation. 2)We present a confidence prediction method to perform reliable multiple imputation. 3)We realize them with GAMIN and train it using novel loss functions based on the confidence. We synthesized highly missing datasets using MNIST and CelebA to perform various experiments. The results show that our method outperforms baseline methods at high missing rate from 80% to 95%.
ISSN:2575-7075
DOI:10.1109/CVPR42600.2020.00848