改进自编码器修复缺失数据，效果优于多数现有方法。
mDAE : modified Denoising AutoEncoder for missing data imputation
- 修改损失函数与超参数选择策略，提升重建精度。
- 在多个UCI数据集上RMSE表现优异，优于8种对比方法。
- 适合需要高精度缺失值填补的科研与工程场景。
本文提出一种基于去噪自编码器（DAE）的缺失数据填补方法，称为mDAE。该方法通过修改损失函数并采用简单直观的超参数选择流程实现改进。在多个UCI机器学习库数据集上的消融实验表明，使用该修改后的损失函数和过完备结构可显著降低重建均方根误差（RMSE）。进一步与四种传统方法及四种较新方法比较，引入“平均最佳距离”（MDB）作为全局性能指标，即各方法在所有数据集上的RMSE与最优方法之间的平均距离。结果表明，mDAE始终位列前茅（与SoftImput、missForest并列），而四种较新方法则系统性排名靠后。相关Python代码将开源，便于复现或推广至其他数据集与方法。
原文摘要 · Abstract (English)
This paper introduces a methodology based on Denoising AutoEncoder (DAE) for missing data imputation. The proposed methodology, called mDAE hereafter, results from a modification of the loss function and a straightforward procedure for choosing the hyper-parameters. An ablation study shows on several UCI Machine Learning Repository datasets, the benefit of using this modified loss function and an overcomplete structure, in terms of Root Mean Squared Error (RMSE) of reconstruction. This numerical study is completed by comparing the mDAE methodology with eight other methods (four standard and four more recent). A criterion called Mean Distance to Best (MDB) is proposed to measure how a method performs globally well on all datasets. This criterion is defined as the mean (over the datasets) of the distances between the RMSE of the considered method and the RMSE of the best method. According to this criterion, the mDAE methodology was consistently ranked among the top methods (along with SoftImput and missForest), while the four more recent methods were systematically ranked last. The Python code of the numerical study will be available on GitHub so that results can be reproduced or generalized with other datasets and methods.
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