| Paper: | MLSP-P5.12 | ||
| Session: | Blind Source Separation III | ||
| Time: | Friday, May 19, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis | ||
| Title: | Methods of Fair Comparison of Performance of Linear ICA Techniques in Presence of Additive Noise | ||
| Authors: | Zbynek Koldovsky, Technical University of Liberec, Czech Republic; Petr Tichavsky, Institute of Information Theory and Automation, Czech Republic | ||
| Abstract: | Linear ICA model with additive Gaussian noise is frequently considered in many practical applications, because it approaches the reality often much better than the noise-free alternate. In this paper, a number important differences between noisy and noiseless ICA are discussed. It is shown that estimation of the mixing/demixing matrix should not be the main goal, in the noisy case. Instead, it is proposed to compare outcome of ICA algorithms with a minimum mean square (MMSE) separation, derived for known mixing model. The signal-to-interference-plus-noise ratio is suggested as the most meaningful performance criterion. A simulation study that compares a few well known ICA algorithms applied to noise data is included. | ||
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