Self-stabilized gradient algorithms for blind source separation with orthogonality constraints
- 1 January 2000
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 11 (6) , 1490-1497
- https://doi.org/10.1109/72.883482
Abstract
Recently, developments in self-stabilized algorithms for gradient adaptation of orthonormal matrices have resulted in simple but powerful principal and minor subspace analysis methods. In this paper, we extend these ideas to develop algorithms for instantaneous prewhitened blind separation of homogeneous signal mixtures. Our algorithms are proven to be self-stabilizing to the Stiefel manifold of orthonormal matrices, such that the rows of the adaptive demixing matrix do not need to be periodically reorthonormalized. Several algorithm forms are developed, including those that are equivariant with respect to the prewhitened mixing matrix. Simulations verify the excellent numerical properties of the proposed methods for the blind source separation task.Keywords
This publication has 19 references indexed in Scilit:
- Flexible independent component analysisPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Extraction of independent components from hybrid mixture: KuicNet learning algorithm and applicationsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Nonholonomic Orthogonal Learning Algorithms for Blind Source SeparationNeural Computation, 2000
- A Fast Fixed-Point Algorithm for Independent Component AnalysisNeural Computation, 1997
- Infomax and maximum likelihood for blind source separationIEEE Signal Processing Letters, 1997
- Neural networks for blind decorrelation of signalsIEEE Transactions on Signal Processing, 1997
- Equivariant adaptive source separationIEEE Transactions on Signal Processing, 1996
- An Information-Maximization Approach to Blind Separation and Blind DeconvolutionNeural Computation, 1995
- On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrixJournal of Mathematical Analysis and Applications, 1985
- Richtungsfelder und Fernparallelismus in n-dimensionalen MannigfaltigkeitenCommentarii Mathematici Helvetici, 1935