Abstract
A novel algorithm is proposed for the nonnegative independent component analysis. In the algorithm, we employ the gradient algorithm with some modifications to separate nonnegative independent sources from mixtures. Since the local convergence of the gradient algorithm is already proved, the result in this paper will be considered one of the convergent nonnegative ICA algorithms. Simulation shows the proposed algorithm can separate the mixtures of nonnegative signals very successfully.
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Keywords
- Independent Component Analysis
- Original Signal
- Independent Component Analysis
- Gradient Algorithm
- Permutation Matrix
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2006 Springer-Verlag Berlin Heidelberg
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Yang, S. (2006). Gradient Algorithm for Nonnegative Independent Component Analysis. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_164
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DOI: https://doi.org/10.1007/11759966_164
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34439-1
Online ISBN: 978-3-540-34440-7
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