REDUCTION ANALYSIS
Because many pattern recognition techniques use very high dimensional feature spaces, and, analysis in these high dimensional spaces is difficult, many pattern recognition techniques reduce dimensionality using principal components analysis (also called Karhunen-Loeve compression).
This transforms the original feature space to a much lower dimensional feature space according to the directions of principal covariance of the training sample.