Is that correct that LDA/FDA can only generate 2 outputs as a dimensional reduction method?
Suppose I have 100 features, I want to reduce to 5 features. Is LDA/FDA not usable?
Is that correct that LDA/FDA can only generate 2 outputs as a dimensional reduction method?
Suppose I have 100 features, I want to reduce to 5 features. Is LDA/FDA not usable?
Applying LDA to data with $K$ classes allows you to project the data onto a $K-1$ dimensional surface in a way that separates the data by class. You cannot arbitrarily choose the number of "features" that the data gets transformed into.
Also, note that you need labels to perform LDA, which is not always available (or relevant). That's why PCA is usually preferred because it's class-agnostic.
The number of output dimensions in LDA is linked to the number of degrees of freedom in the dataset, which is linked to the number of classes $c$: eigenvalues above $c$ will be zero and bear no information (same as with PCA and explained variance).