Common fixed point theorems for weakly generalized contractions and applications on g-metric spaces

Conference proceedings article

Authors/Editors

Strategic Research Themes

No matching items found.

Publication Details

Author list: Yordsorn P., Sumalai P., Borisut P., Kumam P., Cho Y.J.

Publisher: Hindawi

Publication year: 2019

Volume number: 809

Start page: 230

End page: 250

Number of pages: 21

ISBN: 9781728140551

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077955195&doi=10.1109%2fICITEED.2019.8929992&partnerID=40&md5=b9159548c19cf4ecc1e3e4a6e8d27049

Languages: English-Great Britain (EN-GB)

View on publisher site

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