PLSM

Partial Least Squares Feature Extraction

   W = PLSM
   W = PLSM([],MAXLV,METHOD)

   [W, INFORM] = PLSM(A,MAXLV,METHOD)

Input
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'

Output
W	PLS feature extraction mapping
INFORM	extra algorithm output

  DESRIPTION PRTools Adaptation of PLS_TRAIN/PLS_TRANSFORM 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.

Crisp labels will be converted into soft labels which  will be used as a target matrix.

See also

pls_train, pls_transform, pls_apply,