I have a classification problem with only 2 classes, about 100 features and thousands of observations, each belonging to either of the two classes.

Currently I´m doing a PCA prior to machine learning algorithms, which is very successful indeed, so I was wondering whether another dimensionality reduction may be even better than PCA.

What and how could I do dimensionality reduction in a different way than PCA?

I already thought about LDA, but as I have 2 classes only 1 dimension would remain, which would be way too little for correct class prediction on test observations.

Any ideas?