I know PCA and PLS are considered as alternative method to each other. But I am thinking about a kind of combination of the two in case of lots of predictors with little variability.
In that case, when I run 1-component PLS with original predictors, it does not produce a meaningful model in terms of prediction. But if I first compute 10-20 PCA components and run 1-component PLS with those PC scores as predictors, practically, the model is quite good in terms of prediction power. But I would like to know why.
Can anybody explain why this is better than 1-component PLS with original predictors?