The Principal Component Analysis (PCA) is one of the most successful techniques that have

been used in image recognition and compression. PCA is a statistical method under the broad

title of factor analysis. The purpose of PCA is to reduce the large dimensionality of the data

space (observed variables) to the smaller intrinsic dimensionality of feature space (independent

variables), which are needed to describe the data economically. This is the case when there is a

strong correlation between observed variables.