I have got 8 cognitive (continuous) behaviour variables and would like to combine them into a composite score. I would then like to find the best predictors of this outcome (from about 50 predictors).
I was interested if there are alternatives to PCA/Factor analysis or latent variable model approaches which allow to model features which are non-linearly related to the input variables. I am aware of non-linear PCA but as a classical statistician would be interested if there are any other methods in the field of machine learning.
I would also be interested if it is possible to combine the development of a "composite score" and selecting a regression model to predict this composite score simultaneously within a cross-validation model building procedure.
I am grateful about any advice or references.