1. Introduction
Principal component analysis (PCA), which is also known as Karhunen-Loeve (KL) transform, is a classical statistic technique that has been applied to many fields, such as knowledge representation, face recognition and image compression. The objectives of PCA are to reduce the dimensionality of the dataset and identify new meaningful underlying variables. The key idea is to project the objects to an orthogonal subspace for their compact representations. It usually involves a mathematical procedure that transforms a number of correlated variables into a smaller number of uncorrelated variables, which are called principal components. The first principal component accounts for as much of the variability in the dataset as possible, and each succeeding component accounts for as much of the remaining variability as possible. Up to now, there has been an extensive literature that addresses both the theoretical aspect of the PCA method and its application aspects [2], [6], [5].