PLSR Partial Least Squares Regression
W = PLSR
W = PLSR(,MAXLV,METHOD)
[W, INFORM] = PLSR(A,MAXLV,METHOD)
| A|| training dataset|
| MAXLV|| maximal number of latent variables (will be corrected if > rank(A)); MAXLV=inf means MAXLV=min(size(A)) -- theoretical maximum number of LV; by default = inf|
| METHOD|| 'NIPALS' or 'SIMPLS'; by default = 'SIMPLS' |
DESRIPTION PRTools Adaptation of PLS_TRAIN/PLS_APPLY routines. No preprocessing is done inside this mapping. It is the user responsibility to train preprocessing on training data and apply it to the test data.
| W|| PLS feature extraction mapping|
| INFORM|| extra algorithm output|
Crisp labels will be converted into soft labels which will be used as a target matrix.
In order to do regression with the smaller number of latent variables than the number of LV's selected during trainig do d = w.data; d.n = new_n; w.data = d;
pls_train, pls_transform, pls_apply,
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