论文标题
使用深高斯混合物模型估算缺失值的条件密度
Estimating conditional density of missing values using deep Gaussian mixture model
论文作者
论文摘要
我们考虑估计鉴于观察到的值的条件概率分布的问题。我们提出了一种方法，该方法将深神网络的灵活性与高斯混合模型（GMM）的简单性结合在一起。给定数据点不完整，我们的神经网络返回代表相应条件密度的高斯分布的参数（以因子分析仪模型的形式）。我们通过实验验证，与以典型方式训练的条件GMM相比，我们的模型提供了更好的对数可能性。此外，通过使用模型的平均向量替换缺失值获得的插补在视觉上看起来很合理。
We consider the problem of estimating the conditional probability distribution of missing values given the observed ones. We propose an approach, which combines the flexibility of deep neural networks with the simplicity of Gaussian mixture models (GMMs). Given an incomplete data point, our neural network returns the parameters of Gaussian distribution (in the form of Factor Analyzers model) representing the corresponding conditional density. We experimentally verify that our model provides better log-likelihood than conditional GMM trained in a typical way. Moreover, imputation obtained by replacing missing values using the mean vector of our model looks visually plausible.
