In the book Introduction to statistical learning(Section 6.1) it is mentioned that
Forward stepwise selection can be applied even in the high-dimensional setting where n(instances) < p(predictors).
(A least square model to be fit)
But is it not when number of predictors in the incremental selection procedure just increases beyond n, the model thus fit gives infinitely many solutions?
And I won't be able to use this procedure of subset selection beyond n=p.
So my question is , why are the authors telling that forward selection is feasible when p > n?