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Paper data
An Efficient, Robust, and Fast Converging Principal Components Extraction Algorithm: SIPEX-G

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

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.

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