{'fraction_non_alphanumeric': 0.016483516483516484, 'fraction_numerical': 0.0, 'mean_word_length': 5.2844827586206895, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Outliers detection in ICA

Outliers have a significant influence on separate performance of independent component analysis (ICA). Unfortunately, the traditional methods used in ICA do not consider the influence of outliers. In this work an influence function-based detection method is introduced to find the outliers in ICA. Traditional outliers detection techniques can not be directly applied to ICA due to the nature of non-cooperate observed data and limitations of the independent components. This work provides a influence function-based technique to find the outliers in the observed signals. Simulations results show the effectiveness of the proposed approach to detect and establish the outliers in the observed signal.