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.

中文翻译：

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主成分分析

主成分分析 (PCA) 通常用于分析最多样化领域的数据。这项工作以一种易于理解和综合的方式报告了 PCA 的几个理论和实践方面。随后解决了 PCA、数据标准化、PCA 结果的可能可视化和异常值检测的基本原则。接下来，在几个真实世界的数据集上说明了使用 PCA 进行降维的潜力。最后，我们总结了与 PCA 相关的方法和其他降维技术。总而言之，这项工作的目的是帮助来自最不同领域的研究人员使用和解释 PCA。