ACM Computing Surveys· 2021Q1· Review
Principal Component Analysis
- 474citations
- Q1SCImago
- 2021year
Short summary
This review provides an accessible and integrated overview of Principal Component Analysis (PCA), covering its theoretical principles, practical applications like data standardization and outlier detection, and its use for dimensionality reduction on real-world datasets.
AI-generated from the title and abstract; the full text is not read.
Key points
- PCA is a versatile technique applied across diverse fields for data analysis.
- The review covers PCA's theoretical underpinnings, data standardization, and result visualization.
- Practical applications include outlier detection and dimensionality reduction demonstrated on real-world datasets.
- The paper aims to assist researchers in applying and interpreting PCA effectively.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. ACM Computing Surveys, 2021 · DOI ↗
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Field: Analytical Chemistry
Analytical ChemistryChemistry