An efficient, robust, and fast converging principal components extraction algorithm: SIPEX-G

Principal Components Analysis (PCA) is a very important statistical tool in signal processing, which has found successful applications in numerous engineering problems as well as other fields. In general, an on-line algorithm to adapt the PCA network to determine the principal projections of the input data is desired. The authors have recently introduced a fast, robust, and efficient PCA algorithm called SIPEX-G without detailed comparisons and analysis of performance. In this paper, we investigate the performance of SIPEX-G through Monte Carlo runs on synthetic data and on realistic problems where PCA is applied. These problems include direction of arrival estimation and subspace Wiener filtering.