[MRG] New LDA class with shrinkage

OK, here is a new version of the LDA class with (optional) shrinkage. The one in #3105 was already very messy, so I thought I'd start from scratch. By default, nothing has changed and the old SVD-based code is used.

When using the shrinkage approach based on Ledoit Wolf shrinkage, it is important that the data are standardized (centered and scaled to unit standard deviation). Therefore, this really works only once #3521 gets merged too.

Concerning #3500, I have not implemented this yet to avoid adding multiple features in one PR.
Does is give the same performance and decision function? To me it should up to numerical errors. Then you don't need empirical to be supported. 

My 2c