用对抗生成模型修复缺失数据，能处理复杂非线性关系。
Impugan: Learning Conditional Generative Models for Robust Data Imputation
- 设计条件GAN，从完整数据学缺失值与观测值的关系。
- 在基准数据集上使地球移动距离降低82%，信息偏差减少70%。
- 适合处理多源异构、结构复杂的缺失数据场景。
真实世界应用中数据不完整现象普遍，传感器故障、记录不一致及多源数据在尺度、采样率和质量上的差异常导致缺失值，阻碍数据融合与模型构建。传统插补方法如回归、期望最大化及多重插补依赖线性与独立性假设，难以适用于复杂或异构数据，易产生偏差或过度平滑估计。本文提出Impugan，一种用于缺失值插补与异构数据整合的条件生成对抗网络（cGAN）。模型在完整样本上训练，学习缺失变量对可观测变量的依赖关系；推理时，生成器基于可用特征重构缺失项，判别器通过区分真实与生成数据强化结果真实性。该对抗机制可捕捉传统方法无法建模的非线性与多模态关系。在基准数据集与多源集成任务中，Impugan相比领先基线实现最高82%的地球移动距离（EMD）降低与70%的互信息偏差（MI）下降，验证了对抗生成模型在大规模、异构缺失数据插补与融合中的可扩展性与合理性。代码已开源：github.com/zalishmahmud/impuganBigData2025。
原文摘要 · Abstract (English)
Incomplete data are common in real-world applications. Sensors fail, records are inconsistent, and datasets collected from different sources often differ in scale, sampling rate, and quality. These differences create missing values that make it difficult to combine data and build reliable models. Standard imputation methods such as regression models, expectation-maximization, and multiple imputation rely on strong assumptions about linearity and independence. These assumptions rarely hold for complex or heterogeneous data, which can lead to biased or over-smoothed estimates. We propose Impugan, a conditional Generative Adversarial Network (cGAN) for imputing missing values and integrating heterogeneous datasets. The model is trained on complete samples to learn how missing variables depend on observed ones. During inference, the generator reconstructs missing entries from available features, and the discriminator enforces realism by distinguishing true from imputed data. This adversarial process allows Impugan to capture nonlinear and multimodal relationships that conventional methods cannot represent. In experiments on benchmark datasets and a multi-source integration task, Impugan achieves up to 82\% lower Earth Mover's Distance (EMD) and 70\% lower mutual-information deviation (MI) compared to leading baselines. These results show that adversarially trained generative models provide a scalable and principled approach for imputing and merging incomplete, heterogeneous data. Our model is available at: github.com/zalishmahmud/impuganBigData2025
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