| Paper: | MLSP-P1.9 | ||
| Session: | Blind Source Separation II | ||
| Time: | Tuesday, May 16, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis | ||
| Title: | The maximum likelihood approach to complex ICA | ||
| Authors: | Jean-François Cardoso, ENST, France; Tulay Adali, University of Maryland, Baltimore County, United States | ||
| Abstract: | We derive the form of the best non-linear functions for performing complex-valued independent component analysis (ICA) by maximum likelihood estimation. We show that both the form of nonlinearity and the relative gradient update equations for likelihood maximization naturally generalize to the complex case, and that they coincide with the real case. | ||
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