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.