The Principal component analysis, is a standard technique used for data reduction in statistical pattern recognition and signal processing

A common problem in statistical pattern recognition is feature selection or feature extraction. Feature selection is a process whereby a data space is transformed into a feature space that theory has exactly same dimension as the original data space. However the transformation is designed in such a way that the data set is represented by a reduced number of “effective features” and most of the intrinsic information content of the data or the data set undergoes a dimensionality reduction.



PCA