Investigation on the skewness for independent component analysis  

作　　者：LIU ZhiYong QIAO Hong 

机构地区：[1]Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

出　　处：《Science China(Information Sciences)》2011年第4期849-860,共12页中国科学（信息科学）（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant No. 60975002)

摘　　要：Skewness has received much less attention than kurtosis in the independent component analysis （ICA）. In particular, the skewness seems to become a useless statistics after the kurtosis related one-bit-matching theorem was proven. However, as the non-Gaussianity of one signal comes mainly from skewness, it is intuitively understandable that its recovery should not rely on kurtosis. In this paper we discuss the skewness based ICA, and show that any probability density function （pdf） with non-zero skewness can be employed by ICA for the recovery of the source with non-zero skewness, without needing to consider the skewness sign. The observation together with the one-bit-matching theorem provides a basic guideline for the model pdf design in ICA algorithm.Skewness has received much less attention than kurtosis in the independent component analysis （ICA）. In particular, the skewness seems to become a useless statistics after the kurtosis related one-bit-matching theorem was proven. However, as the non-Gaussianity of one signal comes mainly from skewness, it is intuitively understandable that its recovery should not rely on kurtosis. In this paper we discuss the skewness based ICA, and show that any probability density function （pdf） with non-zero skewness can be employed by ICA for the recovery of the source with non-zero skewness, without needing to consider the skewness sign. The observation together with the one-bit-matching theorem provides a basic guideline for the model pdf design in ICA algorithm.

关 键 词：independent component analysis SKEWNESS KURTOSIS one-bit-matching theorem 

分 类 号：O211[理学—概率论与数理统计] TP391.41[理学—数学]