Asymmetric Gaussian and Its Application to Pattern Recognition
Tsuyoshi Kato, Shinichiro Omachi, and Hirotomo Aso
Lecture Notes in Computer Science, vol.2396
(Joint IAPR International Workshops SSPR 2002 and SPR 2002),
pp.405-413, August 2002
Abstract
In this paper, we propose a new probability model, `asymmetric
Gaussian(AG),' which can capture spatially asymmetric distributions.
It is also extended to mixture of AGs. The values of its parameters
can be determined by Expectation-Conditional Maximization algorithm.
We apply the AGs to a pattern classification problem and show that the
AGs outperform Gaussian models.