Missing data imputation (MDI) is crucial when dealing with tabular datasets across various domains. Autoencoders can be trained to reconstruct missing values, and graph autoencoders (GAE) can additionally consider similar patterns in the dataset when imputing new values for a given instance. However, previously proposed GAEs suffer from scalability issues, requiring the user to define a similarity metric among patterns to build the graph connectivity beforehand. In this paper, we leverage recent progress in latent graph imputation to propose a novel EdGe Generation Graph AutoEncoder (EGG-GAE) for missing data imputation that overcomes these two drawbacks. EGG-GAE works on randomly sampled mini-batches of the input data (hence scaling to larger datasets), and it automatically infers the best connectivity across the mini-batch for each architecture layer. We also experiment with several extensions, including an ensemble strategy for inference and the inclusion of what we call prototype nodes, obtaining significant improvements, both in terms of imputation error and final downstream accuracy, across multiple benchmarks and baselines.
翻译:缺少的数据估算( MDI) 在处理不同域的表格数据集时至关重要 。 自动计算器可以接受重建缺失值的培训, 图形自动计算器( GAE) 在为特定实例估算新值时还可以考虑数据集中的类似模式 。 但是, 先前提议的 GAE 存在可缩放问题, 需要用户在图层之间定义相似的度量来预先构建图形连接。 在本文中, 我们利用隐形图形估算的最新进展来提出一个新的 EGE 生成图形自动计算器( EGGG- GAE), 用于弥补这两个缺陷的缺失数据估算 。 EGGG- GAE 在随机抽样的输入数据微型插座上工作( 放大到更大的数据集), 自动推导出每个架构层的小型包的最佳连通性。 我们还在多个扩展中进行实验, 包括用于推推论的混合战略, 并纳入我们称之为原型节点, 获得显著的改进, 包括精度错误和最终下游精确度, 跨越多个基准和基线 。