The HPREDUCE Procedure

The performance of a learning model usually decreases in terms of accuracy and efficiency when the dimensionality of the input data is high. The problem is known as the "curse of dimensionality." Variable selection techniques can reduce the dimensionality of a data set by removing irrelevant and redundant variables (Liu and Motoda 1998).

The HPREDUCE procedure performs both supervised and unsupervised variable selection. It selects variables by identifying a set of variables that can jointly explain the maximum amount of data variance.