Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

February 1, 2006, Vol. 18, No. 2, Pages 430-445
(doi: 10.1162/089976606775093873)
© 2005 Massachusetts Institute of Technology
Differential Log Likelihood for Evaluating and Learning Gaussian Mixtures
Article PDF (126.73 KB)
Abstract

We introduce a new unbiased metric for assessing the quality of density estimation based on gaussian mixtures, called differential log likelihood. As an application, we determine the optimal smoothness and the optimal number of kernels in gaussian mixtures. Furthermore, we suggest a learning strategy for gaussian mixture density estimation and compare its performance with log likelihood maximization for a wide range of real-world data sets.