Principal Component Analysis

DOI Web Site 被引用文献1件
  • Felipe L. Gewers
    Institute of Physics, University of São Paulo, São Paulo, SP, Brazil
  • Gustavo R. Ferreira
    Institute of Mathematics and Statistics, University of São Paulo, São Paulo, SP, Brazil
  • Henrique F. De Arruda
    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
  • Filipi N. Silva
    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
  • Cesar H. Comin
    Department of Computer Science, Federal University of São Carlos, São Carlos, SP, Brazil
  • Diego R. Amancio
    Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, SP, Brazil
  • Luciano Da F. Costa
    São Carlos Institute of Physics, University of São Paulo, São Carlos, SP, Brazil

書誌事項

タイトル別名
  • A Natural Approach to Data Exploration

抄録

<jats:p>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.</jats:p>

収録刊行物

  • ACM Computing Surveys

    ACM Computing Surveys 54 (4), 1-34, 2021-05-24

    Association for Computing Machinery (ACM)

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