用嵌入层替代独热编码，高效生成高基数分类数据
CardiCat: a Variational Autoencoder for High-Cardinality Tabular Data
- 用双编码器嵌入层替代独热编码，联合学习特征表示
- 参数量更少，在真实与模拟数据上生成质量更高
- 适合处理类别多且不均衡的表格数据，如金融、医疗
高基数分类特征是混合类型表格数据的常见特征。现有生成模型在大规模下难以捕捉此类数据的复杂性，主要源于分类特征参数化的困难。本文提出一种通用变分自编码器模型 CardiCat，可准确拟合不平衡、高基数和异构的表格数据。该方法用正则化的双编码器-解码器嵌入层替代独热编码，并联合学习，使嵌入依赖于其他协变量，实现分类特征的紧凑且同质化参数化。模型可训练参数空间显著小于现有方法，支持大规模学习。CardiCat 在多个真实与模拟数据集上生成的合成数据质量优于对比的 VAE 模型，尤其在高基数与不平衡特征上表现更优。
原文摘要 · Abstract (English)
High-cardinality categorical features are a common characteristic of mixed-type tabular datasets. Existing generative model architectures struggle to learn the complexities of such data at scale, primarily due to the difficulty of parameterizing the categorical features. In this paper, we present a general variational autoencoder model, CardiCat, that can accurately fit imbalanced high-cardinality and heterogeneous tabular data. Our method substitutes one-hot encoding with regularized dual encoder-decoder embedding layers, which are jointly learned. This approach enables us to use embeddings that depend also on the other covariates, leading to a compact and homogenized parameterization of categorical features. Our model employs a considerably smaller trainable parameter space than competing methods, enabling learning at a large scale. CardiCat generates high-quality synthetic data that better represent high-cardinality and imbalanced features compared to competing VAE models for multiple real and simulated datasets.
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