Autor: |
Lu, Xuesong, Li, Xiaomeng, Fu, Mao‐sheng, Wang, Haixian |
Zdroj: |
IET Signal Processing (Wiley-Blackwell); Oct2017, Vol. 11 Issue 8, p969-974, 6p |
Abstrakt: |
Blind source separation (BSS) is an active research topic in the fields of biomedical signal processing and brain–computer interface. As a representative technique, maximum signal fraction analysis (MSFA) has been recently developed for the problem of BSS. However, MSFA is formulated based on the L2‐norm, and thus is prone to be negatively affected by outliers. In this study, the authors propose a robust alternative to MSFA based on the L1‐norm, termed as MSFA‐L1. Specifically, they re‐define the objective function of MSFA, in which the energy quantities of both the signal and the noise are defined with the L1‐norm rather than the L2‐norm. By adopting the L1‐norm, MSFA‐L1 alleviates the negative influence of large deviations that are usually associated with outliers. Computationally, they design an iterative algorithm to optimise the objective function of MSFA‐L1. The iterative procedure is shown to converge under the framework of bound optimisation. Experimental results on both synthetic data and real biomedical data demonstrate the effectiveness of the proposed MSFA‐L1 approach. [ABSTRACT FROM AUTHOR] |
Databáze: |
Complementary Index |
Externí odkaz: |
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