2018/04/30 by Felipe L. Gewers, Gustavo R. Ferreira, Henrique Ferraz de Arruda +7 · 6 citations
Chemistry · Computer Science · Mathematics · #Advanced Statistical Methods and Models #Face and Expression Recognition #Spectroscopy and Chemometric Analyses #cs.CE #stat.CO #stat.ME
paper · pdf · doi:10.1145/3447755
published as ACM Computing Surveys (CSUR), 54(4), pp.1-34 (2021)
arxiv created 2018/06/19 · openalex publication_date 2021/05/24 · openalex created_date 2021/06/07 · arxiv updated 2021/06/09 · openalex updated_date 2026/07/26
Principal component analysis (PCA) is often used for analyzing data in the most diverse areas. In this work, we report an integrated approach to several theoretical and practical aspects of PCA. We start by providing, in an intuitive and accessible manner, the basic principles underlying PCA and its applications. Next, we present a systematic, though no exclusive, survey of some representative works illustrating the potential of PCA applications to a wide range of areas. An experimental investigation of the ability of PCA for variance explanation and dimensionality reduction is also developed, which confirms the efficacy of PCA and also shows that standardizing or not the original data can have important effects on the obtained results. Overall, we believe the several covered issues can assist researchers from the most diverse areas in using and interpreting PCA.