Principal Component Analysis

2021/05/24 by Felipe L. Gewers, Gustavo R. Ferreira, Henrique Ferraz de Arruda +4 · 4 citations
Chemistry · Computer Science · Mathematics · #Spectroscopy and Chemometric Analyses #Face and Expression Recognition #Advanced Statistical Methods and Models

paper · doi:10.1145/3447755

openalex publication_date 2021/05/24 · openalex created_date 2021/06/07 · openalex updated_date 2026/07/26

Abstract

Principal component analysis (PCA) is often applied for analyzing data in the most diverse areas. This work reports, in an accessible and integrated manner, several theoretical and practical aspects of PCA. The basic principles underlying PCA, data standardization, possible visualizations of the PCA results, and outlier detection are subsequently addressed. Next, the potential of using PCA for dimensionality reduction is illustrated on several real-world datasets. Finally, we summarize PCA-related approaches and other dimensionality reduction techniques. All in all, the objective of this work is to assist researchers from the most diverse areas in using and interpreting PCA.

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