Yup! i got that point later...anyways reduction of noise can be done in many ways..
as to your second statement
Are the accuracies EXACTLY the same in both cases? I would suspect a bug in the code...
EDIT: Either that, or you validation set is small enough that the accuracies are lining up by random chance
why do u think exactly same cross validation accuracies are not possible for different subsets?
In fact i think this tendency will be more prevalent in subsets with larger features which have lot of noise rather than smaller ones so that a different 'large subset' will eventually result in the same cross validation accuracy...in fact i think it is necessary to look into what exactly is affecting the classifiers accuracy..one is number of informative features ..and anything else?