Principal Component Analysis-Reference-Cited by-同舟云学术

Principal Component Analysis

Published:2022-05-31 Issue:4 Volume:54 Page:1-34
ISSN:0360-0300
Container-title:ACM Computing Surveys
language:en
Short-container-title:ACM Comput. Surv.

Author:

Gewers Felipe L.¹,Ferreira Gustavo R.²,Arruda Henrique F. De³,Silva Filipi N.⁴,Comin Cesar H.⁵,Amancio Diego R.⁶,Costa Luciano Da F.⁷

Affiliation:

1. Institute of Physics, University of São Paulo, São Paulo, SP, Brazil

2. Institute of Mathematics and Statistics, University of São Paulo, São Paulo, SP, Brazil

3. São Carlos Institute of Physics, University of São Paulo, São Carlos, SP, Brazil, Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, SP, Brazil

4. São Carlos Institute of Physics, University of São Paulo, São Carlos, SP, Brazil, School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana 47405, USA

5. Department of Computer Science, Federal University of São Carlos, São Carlos, SP, Brazil

6. Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, SP, Brazil

7. São Carlos Institute of Physics, University of São Paulo, São Carlos, SP, Brazil

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.

Funder

Fundação de amparo à pesquisa do Estado de São Paulo

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Link

https://dl.acm.org/doi/pdf/10.1145/3447755

Reference87 articles.

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