This stack exchange post - https://stats.stackexchange.com/questions/80507/what-is-a-gaussian-discriminant-analysis-gda - discusses GDA, a machine learning method for classification. I would like to implement something like this analysis in R.

I've seen posts for discriminant analysis in R using linear and quadratic discriminant analysis (http://rstudio-pubs-static.s3.amazonaws.com/35817_2552e05f1d4e4db8ba87b334101a43da.html) but nothing for GDA?

I guess my questions are, because I'm not 100% familiar with these methods, (1) whether GDA is different from or a subset of LDA and QDA, and if there is an R package / functions for GDA in R like there are for LDA and QDA (the lda() and qda() functions in the MASS package). Maybe I should be asking if I actually want to be performing GDA with my data (I'm not actually sure if the joint distribution of the features follow a gaussian dist), or if lda or qda may be better?


  • $\begingroup$ From Wikipedia - "Quadratic discriminant analysis (QDA) is closely related to linear discriminant analysis (LDA), where it is assumed that the measurements from each class are normally distributed." My understanding for GDA was that the joint distribution of the variables was supposed to be roughly normally distributed, not each individual variable however. $\endgroup$
    – Canovice
    Nov 24 '16 at 1:41

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