Abstract
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.
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Ye, M., Fan, X., Liu, Q. (2006). Monotonic Convergence of a Nonnegative ICA Algorithm on Stiefel Manifold. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_122
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DOI: https://doi.org/10.1007/11893028_122
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46479-2
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