Determination of the optimal number of features for quadratic discriminant analysis via the normal approximation to the discriminant distribution
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The use of the normal approximation to the discriminant distribution to determine the optimal number of features for quadratic discriminant analysis was discussed. The mean variance of the estimated discriminant was derived and feature-size optimization was compared using the normal approximation to the estimated discriminant with optimization obtained by simulating the true distribution of the estimated discriminant. Optimization via the normal approximation to the estimated discriminant provides huge computational savings in comparison to optimization via simulation of the true distribution. The results show that the feature-size optimization via the normal approximation is very accurate when the covariance matrices differ modestly.
author list (cited authors)
Hua, J., Xiong, Z., & Dougherty, E. R.
complete list of authors
Hua, Jianping||Xiong, Zixiang||Dougherty, Edward R