Activity Number:
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198
- SPEED: Nonparametric Statistics: Estimation, Testing, and Modeling
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2018 : 11:35 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #332674
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Title:
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Multiple Imputation Using Denoising Autoencoders
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Author(s):
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Lovedeep Gondara*
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Companies:
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Keywords:
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Multiple imputation;
missing data;
deep learning;
autoencoders;
neural networks;
nonparametric models
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Abstract:
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Missing data is a significant problem impacting all domains. State-of-the-art framework for minimizing missing data bias is multiple imputation, for which the choice of an imputation model remains nontrivial. We propose a multiple imputation model based on overcomplete deep denoising autoencoders. Our proposed model is capable of handling different data types, missingness patterns, missingness proportions and distributions. Evaluation on several real life datasets show our proposed model significantly outperforms current state-of-the-art methods under varying conditions while simultaneously improving end of the line analytics.
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Authors who are presenting talks have a * after their name.
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