I've 110 variables & 200 data points. Of this 110 variables, one is group variable (say "brown eye","blue eye"). I want to use discriminant analysis to classify the groups based on remaining 119 variables. Since the variables are large, to get a meaningful result I need to reduce the number of variables. So, the are 3 options to me:
1) Stepwise Discriminant Analysis: I don't want to use this method as I'm biased towards it.
2) Classification Tree Method: This method will give an idea about which variables affecting the eye color. Since the dataset is small, I'm apprehensive of using this method.
3) Principal Component Method: This method I can use. But I prefer to keep the original variables.
My question is can anybody please suggest me some other method to select variables for discriminant analysis.