A comparative study and improvement of two ICA using reference signal methods

作者：Mi, Jian Xun^*; Xu, Yong

来源：Neurocomputing, 2014, 137: 157-164.

DOI：10.1016/j.neucom.2013.03.070

• Independent component analysis
• ICA with reference signal
• Wiener filter

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.

• 出版日期2014-8-5
• 单位重庆邮电大学; 哈尔滨工业大学深圳研究生院