Processing of Missing Data by Neural Networks
Abstract
We propose a general, theoretically justified mechanism for processing missing data by neural networks. Our idea is to replace typical neuron's response in the first hidden layer by its expected value. This approach can be applied for various types of networks at minimal cost in their modification. Moreover, in contrast to recent approaches, it does not require complete data for training. Experimental results performed on different types of architectures show that our method gives better results than typical imputation strategies and other methods dedicated for incomplete data.
Cite
Text
Śmieja et al. "Processing of Missing Data by Neural Networks." Neural Information Processing Systems, 2018.Markdown
[Śmieja et al. "Processing of Missing Data by Neural Networks." Neural Information Processing Systems, 2018.](https://mlanthology.org/neurips/2018/smieja2018neurips-processing/)BibTeX
@inproceedings{smieja2018neurips-processing,
title = {{Processing of Missing Data by Neural Networks}},
author = {Śmieja, Marek and Struski, Łukasz and Tabor, Jacek and Zieliński, Bartosz and Spurek, Przemysław},
booktitle = {Neural Information Processing Systems},
year = {2018},
pages = {2719-2729},
url = {https://mlanthology.org/neurips/2018/smieja2018neurips-processing/}
}