Sequential updating algorithm for extracting the basis of karhunen loeve transformation
Yanyun Qu, Zheng Nanning, Cuihua Li, Yuan Zejian · 2005
Karhunen-Loeve transformation (KLT) is a popular method for dimensional reduction and feature extraction in image analysis, signal processing, automatic control systems, and so on, while the drawback of the KLT is an expensive computation. In this paper, we propose a novel updating algorithm for KLT, the rank-k updating algorithm, which has advantages especially for image sequences: it is faster than batch algorithm, it can handle the dynamic database, and it does not save the entire database. Furthermore it makes the active learning and recognition possible in computer vision. Finally we analyze the computational complexity and error of the algorithm. We also show its application in face analysis. The experimental results demonstrate the efficiency of our algorithm.