Global convergence analysis of a PCA learning algorithm

Principal component analysis (PCA) by neural network is an adaptive statistical signal processing method which has many applications. Since many PCA neural networks do not converge globally, it is natural to study the global convergence of the PCA learning algorithm. Previous work on globally convergent PCA neural networks is presented first. Then, based on the mismatch of previous PCA neural networks, we propose and analyze a PCA learning algorithm. This algorithm is convergent globally. A rigorous mathematical proof is given. Simulation results show the efficiency and effectiveness of this algorithm.