Probability density function estimation using the EEF with application to subset/feature selection

Authors

Steven Kay, University of Rhode IslandFollow
Quan Ding, University of California, San Francisco
Bo Tang, University of Rhode Island
Haibo He, University of Rhode IslandFollow

Document Type

Article

Date of Original Version

2-1-2016

Abstract

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.

Publication Title, e.g., Journal

IEEE Transactions on Signal Processing

Volume

64

Issue

3

Citation/Publisher Attribution

Kay, Steven, Quan Ding, Bo Tang, and Haibo He. "Probability density function estimation using the EEF with application to subset/feature selection." IEEE Transactions on Signal Processing 64, 3 (2016): 641-651. doi: 10.1109/TSP.2015.2488591.