论文标题
安装拉普拉斯正规分层高斯模型
Fitting Laplacian Regularized Stratified Gaussian Models
论文作者
论文摘要
我们考虑从数据共同估算多个相关零均值高斯分布的问题。我们建议使用Laplacian正规分层模型拟合共同估算这些协方差矩阵，其中包括每个协方差矩阵的损失和正则化项，也包括鼓励不同协方差矩阵接近的术语。这种方法从邻近的协方差借用了力量，以提高其估计。使用精心选择的超参数，此类模型可以表现出色，尤其是在低数据状态下。我们提出了一种分布式方法，该方法将范围扩展到大问题，并用示例在金融，雷达信号处理和天气预报中说明了该方法的功效。
We consider the problem of jointly estimating multiple related zero-mean Gaussian distributions from data. We propose to jointly estimate these covariance matrices using Laplacian regularized stratified model fitting, which includes loss and regularization terms for each covariance matrix, and also a term that encourages the different covariances matrices to be close. This method `borrows strength' from the neighboring covariances, to improve its estimate. With well chosen hyper-parameters, such models can perform very well, especially in the low data regime. We propose a distributed method that scales to large problems, and illustrate the efficacy of the method with examples in finance, radar signal processing, and weather forecasting.
