Abstract: In the traditional study of independent component analysis (ICA), the outlier data had not been considered. This paper proposes a method based on influence function to find the outliers from the observed data in ICA. General, outliers have a significant influence on the separation performance of ICA. Using the influence functions to project the observed data, the impulsive noisy components which mixed in the observed data can be eliminated from the normal data. The experimental results demonstrate the effectiveness of proposed method.