The problem of estimation of entropy functional of probability densities has received much attention in the information theory machine learning and statistics communities. Kernel density plug-in estimators are simple, easy to implement and widely used for estimation of entropy. However, for large feature dimension d, kernel plug-in estimators suffer from the curse of dimensionality: the MSE rate of convergence is glacially slow - of order O(T-γ/d), where T is the number of samples, and γ > 0 is a rate parameter. In this paper, it is shown that for sufficiently smooth densities, an ensemble of kernel plug-in estimators can be combined via a weighted convex combination, such that the resulting weighted estimator has a superior parametric MSE rate of convergence of order O(T-1). Furthermore, it is shown that these optimal weights can be determined by solving a convex optimization problem which does not require training data or knowledge of the underlying density, and therefore can be performed offline. This novel result is remarkable in that, while each of the individual kernel plug-in estimators belonging to the ensemble suffer from the curse of dimensionality, by appropriate ensemble averaging we can achieve parametric convergence rates.
['Kumar Sricharan', 'Alfred O. Hero']
Ensemble weighted kernel estimators for multivariate entropy estimation