Fully Data-Driven Normalized and Exponentiated Kernel Density Estimator with Hyvärinen Score
基于 Hyvärinen 评分的全数据驱动归一化幂化核密度估计
Recently, Jewson and Rossell proposed a new approach for kernel density estimation using an exponentiated form of kernel density estimators. The density estimator contained two hyperparameters that flexibly controls the smoothness of the resulting density. We tune them in a data-driven manner by minimizing an objective function based on the Hyvärinen score to avoid the optimization involving the intractable normalizing constant caused by the exponentiation. We show the…