Means and Issues for Adjusting Principal Component Analysis Results

Tomokazu Konishi

doi:10.20944/preprints202405.1445.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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)

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.

Principal Component Analysis; rotation matrix, adjusting, unitary matrix

Computer Science and Mathematics, Analysis

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Means and Issues for Adjusting Principal Component Analysis Results

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