Abstract
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.
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Acknowledgment
I would like to address my special acknowledgements to all those people who provide me with data for my experiments. My warm appreciation is due to the Public Authority for Civil Aviation, Directorate General of Meteorology, and Ministry of Tourism in Oman.
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Al-Ruzaiqi, S.K., Dawson, C.W. (2019). New Modification Version of Principal Component Analysis with Kinetic Correlation Matrix Using Kinetic Energy. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication Networks. FICC 2018. Advances in Intelligent Systems and Computing, vol 886. Springer, Cham. https://doi.org/10.1007/978-3-030-03402-3_30
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DOI: https://doi.org/10.1007/978-3-030-03402-3_30
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