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New modification version of principal component analysis with kinetic correlation matrix using kinetic energy

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conference contribution
posted on 12.06.2018, 13:59 by Sara Al-Ruzeiqi, Christian DawsonChristian Dawson
Principle Component Analysis (PCA) is a direct, non-parametric method for extracting pertinent information from confusing data sets. It presents a roadmap for how to reduce a complex data set to a lower dimension to disclose the hidden, simplified structures that often underlie it. However, most PCA methods are not able to realize the desired benefits when they handle real world, and nonlinear data. In this work, a modified version of PCA with kinetic correlation matrix using kinetic energy is proposed. The features of this modified PCA have been assessed on different data sets of air passenger numbers. The results show that the modified version of PCA is more effective in data compression, classes reparability and classification accuracy than using traditional PCA.

History

School

  • Science

Department

  • Computer Science

Published in

Future of Information and Communication Conference (FICC) 2018

Citation

AL-RUZEIQI, S.K. and DAWSON, C.W., 2018. New modification version of principal component analysis with kinetic correlation matrix using kinetic energy. IN: Arai K., Kapoor S. and Bhatia R. (eds). Advances in Information and Communication Networks: Proceedings of the 2018 Future of Information and Communication Conference (FICC), Vol. 1., Singapore, Singapore, 5-6 April 2018, pp.438-450.

Publisher

© Springer

Version

AM (Accepted Manuscript)

Acceptance date

20/09/2017

Publication date

2018

Notes

This is a pre-copyedited version of a contribution published in Arai K., Kapoor S. and Bhatia R. (eds). Advances in Information and Communication Networks: Proceedings of the 2018 Future of Information and Communication Conference (FICC), Vol. 1. published by Springer . The definitive authenticated version is available online via https://doi.org/10.1007/978-3-030-03402-3_30

ISBN

9783030034016;9783030034023

Book series

Advances in Intelligent Systems and Computing;886

Language

en

Location

Singapore