Summary of Mdae : Modified Denoising Autoencoder For Missing Data Imputation, by Mariette Dupuy et al.
mDAE : modified Denoising AutoEncoder for missing data imputation
by Mariette Dupuy, Marie Chavent, Remi Dubois
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed methodology, called mDAE, is a modification of Denoising AutoEncoder (DAE) for missing data imputation. It uses a modified loss function and a straightforward procedure to choose hyper-parameters. The benefits of using this approach are demonstrated through an ablation study on several UCI Machine Learning Repository datasets, showing improvements in Root Mean Squared Error (RMSE) of reconstruction compared to standard DAE. A criterion called Mean Distance to Best (MDB) is introduced to measure global performance across datasets. The mDAE methodology is found to be consistently ranked among the top methods, outperforming some more recent approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to fill in missing data using a type of machine learning model called Denoising AutoEncoder (DAE). This method, called mDAE, works better than usual DAE on certain types of datasets. The researchers tested it on several different datasets and showed that it does a good job of reconstructing the missing data. |
Keywords
» Artificial intelligence » Autoencoder » Loss function » Machine learning
