Convergence of the symmetrical FastICA algorithm
- 6 May 2004
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
The FastICA algorithm is one of the most popular methods to solve problems in independent component analysis (ICA) and blind source separation. It has been shown experimentally that it outperforms most of the commonly used ICA algorithms in convergence speed. A rigorous convergence analysis has been presented only for the so-called one-unit case, in which just one of the rows of the separating matrix is considered. However, in the FastICA algorithm, there is also an explicit normalization step, and it may be questioned whether the extra rotation caused by the normalization will effect the convergence speed. The purpose of this paper is to show that this is not the case and the good convergence properties of the one-unit case are also shared by the full algorithm with symmetrical normalization.Keywords
This publication has 5 references indexed in Scilit:
- Fast and robust fixed-point algorithms for independent component analysisIEEE Transactions on Neural Networks, 1999
- AN EXPERIMENTAL COMPARISON OF NEURAL ALGORITHMS FOR INDEPENDENT COMPONENT ANALYSIS AND BLIND SEPARATIONInternational Journal of Neural Systems, 1999
- A Fast Fixed-Point Algorithm for Independent Component AnalysisNeural Computation, 1997
- Equivariant adaptive source separationIEEE Transactions on Signal Processing, 1996
- An Information-Maximization Approach to Blind Separation and Blind DeconvolutionNeural Computation, 1995