Artificial Neural Networks with Random Weights for Incomplete Datasets

Diego P.P. Mesquita, João Paulo P. Gomes*, Leonardo R. Rodrigues

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)

Abstract

In this paper, we propose a method to design Neural Networks with Random Weights in the presence of incomplete data. We present a method, under the general assumption that the data is missing-at-random, to estimate the weights of the output layer as a function of the uncertainty of the missing data estimates. The proposed method uses the Unscented Transform to approximate the expected values and the variances of the training examples after the hidden layer. We model the input data as a Gaussian Mixture Model with parameters estimated via a maximum likelihood approach. The validity of the proposed method is empirically assessed under a range of conditions on simulated and real problems. We conduct numerical experiments to compare the performance of the proposed method to the performance of popular, parametric and non-parametric, imputation methods. By the results observed in the experiments, we conclude that our proposed method consistently outperforms its counterparts.

Original languageEnglish
Number of pages28
JournalNeural Processing Letters
DOIs
Publication statusPublished - 1 Jan 2019
MoE publication typeA1 Journal article-refereed

Keywords

  • Missing data
  • Neural networks
  • Unscented transform

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