Efficient Methods for Dealing with Missing Data in Supervised Learning
Abstract
We present efficient algorithms for dealing with the problem of mis(cid:173) sing inputs (incomplete feature vectors) during training and recall. Our approach is based on the approximation of the input data dis(cid:173) tribution using Parzen windows. For recall, we obtain closed form solutions for arbitrary feedforward networks. For training, we show how the backpropagation step for an incomplete pattern can be approximated by a weighted averaged backpropagation step. The complexity of the solutions for training and recall is independent of the number of missing features. We verify our theoretical results using one classification and one regression problem.
Cite
Text
Tresp et al. "Efficient Methods for Dealing with Missing Data in Supervised Learning." Neural Information Processing Systems, 1994.Markdown
[Tresp et al. "Efficient Methods for Dealing with Missing Data in Supervised Learning." Neural Information Processing Systems, 1994.](https://mlanthology.org/neurips/1994/tresp1994neurips-efficient/)BibTeX
@inproceedings{tresp1994neurips-efficient,
title = {{Efficient Methods for Dealing with Missing Data in Supervised Learning}},
author = {Tresp, Volker and Neuneier, Ralph and Ahmad, Subutai},
booktitle = {Neural Information Processing Systems},
year = {1994},
pages = {689-696},
url = {https://mlanthology.org/neurips/1994/tresp1994neurips-efficient/}
}