Name
Title | ||
|---|---|---|
Probability Density Function Estimation Using the EEF With Application to Subset/Feature Selection |
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TSP.2015.2488591 | IEEE Trans. Signal Processing |
Keywords | Field | DocType |
feature selection,probability,regression analysis,set theory,EEF,Kullback-Liebler distance,PDF,error metric,exponentially embedded family,feature selection,linear regression,machine learning,multipath estimation,probability density function estimation,subset selection,Density estimation robust algorithm,exponentially embedded family,feature selection,parameter estimation,regression analysis | Density estimation,Multivariate kernel density estimation,Pattern recognition,Feature selection,Exponential family,Artificial intelligence,Estimation theory,Sufficient statistic,Mathematics,Estimator,Kernel (statistics) | Journal |
Volume | Issue | ISSN |
64 | 3 | 1053-587X |
Citations | PageRank | References |
7 | 0.47 | 3 |
Authors | ||
4 | ||