I have a matrix with 1024 features and 10000 samples with a label vector of three different classes. I use R MASS library
lda function to compute the model and get the coefficients for linear discriminants:
> library (MASS) > > ldamodel = lda(data$X, data$y) > head (ldamodel$scaling) LD1 LD2 V5 -0.053074978 0.14565211 V6 -0.009618016 -0.11198306 V7 -0.003863230 0.28189459 V8 0.063191889 -0.26726050 V9 -0.029950632 0.16121364 V10 0.017988965 -0.01584389 ...
The model has two linear discriminants, which is ok. But I'd like to know, if it is possible make the
lda function create more than two linear discriminants and how to do it in R? I'm not yet very faimiliar with LDA, so I'm sorry, if this question is a statistical nonsense.