论文标题
缺少深层分层模型和哈密顿蒙特卡洛的数据插补和获取
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo
论文作者
论文摘要
变化自动编码器（VAE）最近在归类和获取异质缺失数据方面非常成功。但是，在此特定的应用域中，仅使用一层潜在变量和严格的高斯后近似值来限制现有的VAE方法。为了解决这些局限性，我们提出了HH-VAEM，这是一种用于混合型不完整数据的分层VAE模型，该模型使用Hamiltonian Monte Carlo带有自动超参数调整以改善近似推断。我们的实验表明，HH-VAEM在缺少数据插图和有监督的学习缺失功能的任务中优于现有基准。最后，我们还提出了一种基于抽样的方法，用于在使用HH-VAEM获取缺失功能时有效地计算信息增益。我们的实验表明，基于抽样的方法优于基于高斯近似值的替代方法。
Variational Autoencoders (VAEs) have recently been highly successful at imputing and acquiring heterogeneous missing data. However, within this specific application domain, existing VAE methods are restricted by using only one layer of latent variables and strictly Gaussian posterior approximations. To address these limitations, we present HH-VAEM, a Hierarchical VAE model for mixed-type incomplete data that uses Hamiltonian Monte Carlo with automatic hyper-parameter tuning for improved approximate inference. Our experiments show that HH-VAEM outperforms existing baselines in the tasks of missing data imputation and supervised learning with missing features. Finally, we also present a sampling-based approach for efficiently computing the information gain when missing features are to be acquired with HH-VAEM. Our experiments show that this sampling-based approach is superior to alternatives based on Gaussian approximations.
