Probability Density Function Estimation Using the EEF With Application to Subset/Feature Selection

We describe a method for estimating a probability density function when some of the sufficient statistics are known. The general form of the PDF is within the exponential family and is known as the exponentially embedded family. Using the proposed estimator new approaches to choosing features from a set of possible features become available, with the error metric being the Kullback-Liebler distance. Applications to subset selection in the context of multipath estimation as well as linear regression for machine learning are used to illustrate the practical utility of the method.