Abstract
In this paper, we present a comparative assessment of two ICA using reference signal methods. Independent Component Analysis (ICA) using reference signal is a useful tool for extracting a desired independent component (IC). Reference signal is served as a priori information to conduct the one-unit ICA to converge to the local extreme point related to a desired IC. There are two methods can perform ICA using reference signal, namely ICA with reference (ICA-R) and fast ICA with reference signal (FICAR). This paper intends to fill in a gap in the previous studies to give comparisons of those methods systematically. Moreover, we also provide useful improvements to the two methods. Firstly, we propose a improved algorithm to fix the flaw in the previous FICAR. Secondly, for ICA-R, we propose a criterion to select appropriate distance measurement.
| Original language | English |
|---|---|
| Pages (from-to) | 157-164 |
| Number of pages | 8 |
| Journal | Neurocomputing |
| Volume | 137 |
| DOIs | |
| State | Published - 5 Aug 2014 |
| Externally published | Yes |
Keywords
- ICA with reference signal
- Independent component analysis
- Wiener filter
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