| Paper: | MLSP-P1.10 | ||
| Session: | Blind Source Separation and ICA | ||
| Time: | Tuesday, May 18, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis | ||
| Title: | A EUCLIDEAN DIRECTION BASED ALGORITHM FOR BLIND SOURCE SEPARATION USING A NATURAL GRADIENT | ||
| Authors: | Glen Mabey; Utah State University | ||
| Jacob Gunther; Utah State University | |||
| Tamal Bose; Utah State University | |||
| Abstract: | The development in this paper is a extension of the adaptive RLStype algorithm proposed by Zhu and Zhang. Their work uses the matrix inversion lemma to iteratively solve the equation obtained from the natural gradient of the nonlinear principle component analysis problem. This paper reduces the complexity of the solution by applying the Euclidean Direction Search concept in place of the matrix inversion lemma. The simulations performed show that the convergence rate is comparable, albeit slower, but with reduced complexity per iteration. | ||
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