论文标题
VAEM：一种用于异质混合类型数据的深层生成模型
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
论文作者
论文摘要
由于自然数据集的异质性，深层生成模型通常在现实世界中的性能很差。异质性来自包含不同类型的特征（分类，序数，连续等）的数据以及具有不同边缘分布的相同类型的特征。我们提出了一个称为VAEM的变异自动编码器（VAE）的扩展，以处理这种异质数据。 VAEM是一个以两个阶段的方式训练的深层生成模型，因此第一阶段将数据的表示更加统一到第二阶段，从而避免了由异质数据引起的问题。我们提供VAEM的扩展，以处理部分观察到的数据，并在数据生成，缺少数据预测和顺序特征选择任务中演示其性能。我们的结果表明，VAEM扩大了可以成功部署深层生成模型的现实应用程序的范围。
Deep generative models often perform poorly in real-world applications due to the heterogeneity of natural data sets. Heterogeneity arises from data containing different types of features (categorical, ordinal, continuous, etc.) and features of the same type having different marginal distributions. We propose an extension of variational autoencoders (VAEs) called VAEM to handle such heterogeneous data. VAEM is a deep generative model that is trained in a two stage manner such that the first stage provides a more uniform representation of the data to the second stage, thereby sidestepping the problems caused by heterogeneous data. We provide extensions of VAEM to handle partially observed data, and demonstrate its performance in data generation, missing data prediction and sequential feature selection tasks. Our results show that VAEM broadens the range of real-world applications where deep generative models can be successfully deployed.
