research-article

Principal Component Analysis: A Natural Approach to Data Exploration

Authors:

Felipe L. Gewers,

Gustavo R. Ferreira,

Henrique F. De Arruda,

Filipi N. Silva,

Cesar H. Comin,

Diego R. Amancio,

Luciano Da F. CostaAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 4

Article No.: 70, Pages 1 - 34

https://doi.org/10.1145/3447755

Published: 24 May 2021 Publication History

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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Principal Component Analysis: A Natural Approach to Data Exploration

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 4

May 2022

782 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3464463

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 24 May 2021

Accepted: 01 January 2021

Revised: 01 September 2020

Received: 01 August 2018

Published in CSUR Volume 54, Issue 4

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