Paper data
Title:
An Efficient, Robust, and Fast Converging Principal Components Extraction Algorithm: SIPEX-G Author(s): Erdogmus Deniz, University of Florida Rao Yadunandana N., University of Florida Príncipe José, University of Florida Fontenla-Romero Oscar, Universidad de A Coruña Vielva Luis, Universidad de Cantabria Page numbers in the proceedings: Volume II pp 335-338 Session: Signal Reconstruction
Paper abstract
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.
Paper
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