A New Method of Fast-complete Matrix-projection Principal Component Analysis
基于矩阵完备投影的快速主分量分析算法

Principal component analysis(PCA) is a well-known method in pattern recognition.But the classical PCA transforms original image matrices into same dimensional vectors which will result in very large dimension of covariance matrix and very high computational complexity when processing image matrices.Moreover,extracted feature of the images are not excellent due to the fact that thepixel's spatial relativity based on the classical PCA was neglected.This paper presents a fast-complete matrix-projection principal component analysis(FMPCA) that decreases the computational complexity and utilizes the spatial relativity between rows and columns.The experiments conducted on NUST603,Yale and ORL face database demonstrate that the proposed algorithm can not only extract image feature efficiently but also maintain more powerful and excellent performance than some other principal component analysis methods.