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Download fileNew modification version of principal component analysis with kinetic correlation matrix using kinetic energy
conference contribution
posted on 2018-06-12, 13:59 authored by Sara Al-Ruzeiqi, Christian DawsonChristian DawsonPrinciple 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) 2018Citation
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
© SpringerVersion
- AM (Accepted Manuscript)
Acceptance date
2017-09-20Publication date
2018Notes
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_30ISBN
9783030034016;9783030034023Publisher version
Book series
Advances in Intelligent Systems and Computing;886Language
- en