How is the performance of Fischer projection compared to other LDA methods of dimension reduction? I thought that Fischer projection was a great method of dimension reduction by maximizing class separation, but when I looked at the LDA methods in scikit learn, Fischer projection wasn't even in the list. This got me thinking, is it any good compared to the other methods out there?

Edit (answer): My bad, they are the same. Fischer projection is a 2 class special case of LDA. Projection using LDA can be performed using the fit_transform and transform methods in sklearn


I didn't get what you meant by "other LDA Methods". To the best of my knowledge, Fisher method is just one.

Sklearn has the implementation and you can find it here.

Is it good or bad? When it comes to dimensionality reduction you don't know as dimensionality reduction methods are usually unsupervised. So if PCA works better? No one knows. At least in the context of two-class classification, Fisher method is still brilliant.

Hope it helped!

  • $\begingroup$ In the scikit learn's implementation for the projection, is there a reason why you can only project to < (no_classes - 1) dimensions? $\endgroup$
    – Sid
    Mar 19 '18 at 6:12
  • $\begingroup$ stats.stackexchange.com/a/181959/97464 $\endgroup$ Mar 19 '18 at 11:34

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