# Principal Component Analysis: A Natural Approach to Data Exploration

arXiv: https://arxiv.org/abs/1804.02502 · HTML: https://mlsanity.com/paper/1804.02502/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2018.

## Built on by (most cited first, 9 of 9)

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- [Pluto's Surface Mapping using Unsupervised Learning from Near-Infrared Observations of LEISA/Ralph](https://mlsanity.com/paper/2301.06027/) (2023)
- [Efficient Semi-Supervised Adversarial Training via Latent Clustering-Based Data Reduction](https://mlsanity.com/paper/2501.10466/) (2025)
- [Federated Structured Sparse PCA for Anomaly Detection in IoT Networks](https://mlsanity.com/paper/2503.23981/) (2025)
- [Defending Against Beta Poisoning Attacks in Machine Learning Models](https://mlsanity.com/paper/2508.01276/) (2025)

## Abstract (arXiv)

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.