Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Natural gradient works efficiently in learning
Neural Computation
The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
A Note on Stone's Conjecture of Blind Signal Separation
Neural Computation
An adaptive blind signal separation based on the joint optimization of Givens rotations
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
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Blind Source Separation (BSS) problems generally can be simplified as an optimization model with orthogonal constraints. Addressing it, natural gradient algorithms are often used. But this kind of algorithm converges relatively slowly and the separation accuracy is sensitive to step size parameter. An adaptive Givens rotations algorithm is proposed in this paper in order to make faster convergence and do not need any more step size parameters. Simulations show that the proposed algorithm is a robust and promising method in BSS comparing with the other similar problems.