Nonlinear systems analysis (2nd ed.)
Nonlinear systems analysis (2nd ed.)
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
Blind separation of positive sources by globally convergent gradient search
Neural Computation
A Theory for Learning by Weight Flow on Stiefel-Grassman Manifold
Neural Computation
On the discrete-time dynamics of the basic Hebbian neural network node
IEEE Transactions on Neural Networks
Algorithms for nonnegative independent component analysis
IEEE Transactions on Neural Networks
Global convergence analysis of a discrete time nonnegative ICA algorithm
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
When the independent sources are known to be nonnegative and well-grounded, which means that they have a non-zero pdf in the region of zero, a few nonnegative independent component analysis (ICA) algorithms have been proposed to separate these positive sources. In this paper, by using the property of skew-symmetry matrix, rigorous convergence proof of a nonnegative ICA algorithm on Stiefel manifold is given. And sufficient convergence conditions are presented. Simulations are employed to confirm our convergence theory. Our techniques may be useful to analyze general ICA algorithms on Stiefel manifold.