Component reduction in linear discriminant analysis
The linear discriminant function is transformed into a linear combination of independent random variables. It is shown that reducing dimensionality, using the smallest distance criterion results in smaller increase in the error rate than using the smallest valiance criterion. Three error rates are used to prove this.
smallest distance criterion smallest variance criterion optimum error rate plug-in error rate principal components