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Means and Issues for Adjusting Principal Component Analysis Results
Version 1
: Received: 20 May 2024 / Approved: 22 May 2024 / Online: 22 May 2024 (12:55:27 CEST)
Version 2 : Received: 27 May 2024 / Approved: 27 May 2024 / Online: 27 May 2024 (22:30:09 CEST)
Version 2 : Received: 27 May 2024 / Approved: 27 May 2024 / Online: 27 May 2024 (22:30:09 CEST)
How to cite: Konishi, T. Means and Issues for Adjusting Principal Component Analysis Results. Preprints 2024, 2024051445. https://doi.org/10.20944/preprints202405.1445.v1 Konishi, T. Means and Issues for Adjusting Principal Component Analysis Results. Preprints 2024, 2024051445. https://doi.org/10.20944/preprints202405.1445.v1
Abstract
Principal Component Analysis (PCA) is a method that identifies common directions within multivariate data and presents the data in as few dimensions as possible. One of the advantages of PCA is its objectivity, as the same results can be obtained regardless of who performs the analysis. However, PCA is not a robust method and is sensitive to noise. Consequently, the directions identified by PCA may deviate slightly. If we can teach PCA to account for this deviation and correct it, the results should become more comprehensible. The methods for doing this and an issue with this are presented.
Keywords
Principal Component Analysis; rotation matrix, adjusting, unitary matrix
Subject
Computer Science and Mathematics, Analysis
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.
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