Version 1
: Received: 15 July 2023 / Approved: 17 July 2023 / Online: 17 July 2023 (11:21:33 CEST)
How to cite:
Kalantan, Z.I.; Alharbi, L.; Binhimd, S. Bootstrap Approach for Principal Component Analysis Method; Cases studies. Preprints2023, 2023071100. https://doi.org/10.20944/preprints202307.1100.v1
Kalantan, Z.I.; Alharbi, L.; Binhimd, S. Bootstrap Approach for Principal Component Analysis Method; Cases studies. Preprints 2023, 2023071100. https://doi.org/10.20944/preprints202307.1100.v1
Kalantan, Z.I.; Alharbi, L.; Binhimd, S. Bootstrap Approach for Principal Component Analysis Method; Cases studies. Preprints2023, 2023071100. https://doi.org/10.20944/preprints202307.1100.v1
APA Style
Kalantan, Z.I., Alharbi, L., & Binhimd, S. (2023). Bootstrap Approach for Principal Component Analysis Method; Cases studies. Preprints. https://doi.org/10.20944/preprints202307.1100.v1
Chicago/Turabian Style
Kalantan, Z.I., Lujain Alharbi and Sulafah Binhimd. 2023 "Bootstrap Approach for Principal Component Analysis Method; Cases studies" Preprints. https://doi.org/10.20944/preprints202307.1100.v1
Abstract
Analyzing big data poses a great challenge for numerous researchers to explore the data structure. Dimension reduction methods can be used to reduce data dimensionality, taking it from occupying a high-dimensional space to existing in a lower-dimensional space while retaining as much information as possible. Principal Component Analysis is one of the most popular used to reduce the dimensional space. The bootstrap sample is obtained by randomly sampling n times with replacement from the original sample, the method provides easy tool to understand the interactive component and develop the process. There are not enough researches discussed the stability of PCA method using bootstrap method. In this paper, the bootstrap method is used to analyze the stability of PCA results and to estimate the number of PCA in efficient way. The method is used to estimate the number of PCA that needed to classify the data set, and the effectiveness of the discussed techniques is demonstrated through real data sets.
Keywords
principal component analysis; bootstrap; stability
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
