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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Principal Component Analysis: A Natural Approach to Data Exploration

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Author(s):
Gewers, Felipe L. [1] ; Ferreira, Gustavo R. [2] ; De Arruda, Henrique F. [3, 4] ; Silva, Filipi N. [5, 6, 7] ; Comin, Cesar H. [8] ; Amancio, Diego R. [9] ; Costa, Luciano Da F. [4]
Total Authors: 7
Affiliation:
[1] Univ Sao Paulo, Inst Phys, Sao Paulo Vila Univ, 187 Rua Matto, BR-05508090 Sao Paulo, SP - Brazil
[2] Univ Sao Paulo, Inst Math & Stat, Sao Paulo Vila Univ, 1010 Rua Matao, BR-05508090 Sao Paulo, SP - Brazil
[3] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil
[4] Univ Sao Paulo, Sao Carlos Inst Phys, FCM, 400 Trabalhador Sao Carlense Ave, BR-13566590 Sao Carlos, SP - Brazil
[5] Univ Sao Paulo, Sao Carlos Inst Phys, Sao Carlos, SP - Brazil
[6] Indiana Univ, Sch Informat Comp & Engn, Bloomington, IL 47405 - USA
[7] Indiana Univ, Network Sci Inst, 1001 IN 45, Bloomington, IN 47408 - USA
[8] Univ Fed Sao Carlos, Dept Comp Sci, 235 Washington Luiz Ave, BR-13565905 Sao Carlos, SP - Brazil
[9] Univ Sao Paulo, Inst Math & Comp Sci, 400 Trabalhador Sao Carlense Ave, BR-13566590 Sao Paulo, SP - Brazil
Total Affiliations: 9
Document type: Journal article
Source: ACM COMPUTING SURVEYS; v. 54, n. 4 JUL 2021.
Web of Science Citations: 1
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. (AU)

FAPESP's process: 17/13464-6 - Modelling citation and information graphs: a complex network approach
Grantee:Diego Raphael Amancio
Support Opportunities: Scholarships abroad - Research
FAPESP's process: 18/09125-4 - Representation, characterization and modeling of biological images using complex networks
Grantee:Cesar Henrique Comin
Support Opportunities: Regular Research Grants
FAPESP's process: 15/22308-2 - Intermediate representations in Computational Science for knowledge discovery
Grantee:Roberto Marcondes Cesar Junior
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 16/19069-9 - Using semantical information to classify texts modelled as complex networks
Grantee:Diego Raphael Amancio
Support Opportunities: Regular Research Grants
FAPESP's process: 18/10489-0 - Transformations of complex networks and their implication in topology and dynamics of complex systems
Grantee:Henrique Ferraz de Arruda
Support Opportunities: Scholarships in Brazil - Post-Doctoral
FAPESP's process: 11/50761-2 - Models and methods of e-Science for life and agricultural sciences
Grantee:Roberto Marcondes Cesar Junior
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 19/16223-5 - Analysis of Opinion Dynamics in Terms of Structure and Dynamics of Complex Networks
Grantee:Henrique Ferraz de Arruda
Support Opportunities: Scholarships abroad - Research Internship - Post-doctor