NIPS Proceedingsβ

Processing of missing data by neural networks

Part of: Advances in Neural Information Processing Systems 31 (NIPS 2018) pre-proceedings

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Authors

Conference Event Type: Poster

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.