Common fixed point results for fuzzy mappings on complex-valued metric spaces with homotopy results
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Author list: Humaira, Sarwar M., Kumam P.
Publisher: Hindawi
Publication year: 2019
Volume number: 11
Issue number: 1
ISBN: 9781728140551
ISSN: 0146-9428
eISSN: 1745-4557
Languages: English-Great Britain (EN-GB)
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Abstract
Feature selection is one of the commonly used technique in machine learning literature. It aims to reduce irrelevant, redundant, unneeded attributes from data that do not contribute to improve or even decrease the performance of analytical model. This paper proposes a new feature selection method that evaluate by back-propagated weighting the nongaussianity, Kurtosis, of the corresponding independent components. The nongaussianity scores are normalized using a suitable logistic function where the parameters of the logistic function are selected using an auto fitting curve technique. This proposed method is called the Logistic function of Kurtosis of Independent Component Analysis (KL-ICA). The results on various benchmarks show significant improvement of analytical model performance over existing technique. ฉ 2019 IEEE.
Keywords
Independent Component Analysis(ICA), Kurtosis, Kutosis and Logistic Independent Component Analysis(KL-ICA), Logistic Function, Unsupervised Learning
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