Volume 4, Issue 1, June 2019, Page: 1-7
Reducing Computational Time of Principal Component Analysis with Chinese Remainder Theorem
Madandola Tajudeen Niyi, Department of Computer Science, Kwara State College of Education, Oro, Nigeria
Gbolagade Kazeem Alagbe, Department of Computer Science, Kwara State University, Malete, Nigeria
Received: Feb. 1, 2019;       Accepted: Mar. 12, 2019;       Published: Mar. 30, 2019
DOI: 10.11648/j.dmath.20190401.11      View  28      Downloads  12
Abstract
It is of paramount importance to establish an identity of citizenry to curb criminalities. Principal Component Analysis (PCA) which is one of the foremost methods for feature extraction and feature selection is adopted for identification and authentication of people. The computational time used by PCA is too much and Chinese Remainder Theorem was employed to reduce its computational time. TOAM database was setup which contained 120 facial images of 40 persons frontal faces with 3 images of each individual. 80 images were used for training while 40 were used for testing. Training time and testing time were used as performance metrics to determine the effect of CRT on PCA in terms of computational time. The experimenal results indicated an average training time of 13.5128 seconds and average testing time of 1.5475 second for PCA while PCA-CRT average training time is 13.2387 seconds and average testing time of 1.5185 seconds. Column chart was used to show the graphical relationship between PCA and PCA-CRT Training time and testing time. The research revealed that CRT reduce PCA computational time.
Keywords
Dimensionality Reduction, Chinese Remainder Theorem, Eigenface, Training Time, Database
To cite this article
Madandola Tajudeen Niyi, Gbolagade Kazeem Alagbe, Reducing Computational Time of Principal Component Analysis with Chinese Remainder Theorem, International Journal of Discrete Mathematics. Vol. 4, No. 1, 2019, pp. 1-7. doi: 10.11648/j.dmath.20190401.11
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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