Principal Component Analysis

@article{Gewers2021PrincipalCA,
  title={Principal Component Analysis},
  author={Felipe L. Gewers and Gustavo R. Ferreira and Henrique Ferraz de Arruda and Filipi Nascimento Silva and C{\'e}sar Henrique Comin and Diego Raphael Amancio and Luciano da Fontoura Costa},
  journal={ACM Computing Surveys (CSUR)},
  year={2021},
  volume={54},
  pages={1 - 34}
}
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… 
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References

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TLDR
This work introduces a new method called sparse principal component analysis (SPCA) using the lasso (elastic net) to produce modified principal components with sparse loadings and shows that PCA can be formulated as a regression-type optimization problem.
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Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based on a probability model. We demonstrate how the principal axes of a set of…
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TLDR
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