In a supervised binary classification task we have a total of 15 fea-tures, among which only 4 are useful for predicting the target vari-able, the other features are pure random noise with very high vari-ance. What complicates matters even worse is that the 4 featureswhen considered individually show no predictive power, and only work when considered together.Consider each of the following dimension reduction (or classifica-tion techniques) and indicate whether it may be able to successfullyide