mlsanityData to 7 Oct 2026

A Tutorial on Principal Component Analysis

arXiv 1404.1100 ↗First posted 3 Apr 2014cs.LG stat.MLcited by 139

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Abstract (arXiv)

Principal component analysis (PCA) is a mainstay of modern data analysis - a black box that is widely used but (sometimes) poorly understood. The goal of this paper is to dispel the magic behind this black box. This manuscript focuses on building a solid intuition for how and why principal component analysis works. This manuscript crystallizes this knowledge by deriving from simple intuitions, the mathematics behind PCA. This tutorial does not shy away from explaining the ideas informally, nor does it shy away from the mathematics. The hope is that by addressing both aspects, readers of all levels will be able to gain a better understanding of PCA as well as the when, the how and the why of applying this technique.

Key facts. Cited by 139 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Apr 2014.

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Citing papers per year: 2021: 16; 2022: 20; 2023: 22; 2024: 26; 2025: 28; 2026: 27.

139 papers.

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  12. 2023Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Datasetcited by 17
38 more
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  9. 2022A Unified Multimodal De- and Re-coupling Framework for RGB-D Motion Recognitioncited by 9
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  22. 2021A Comparison of Word2Vec, HMM2Vec, and PCA2Vec for Malware Classificationcited by 4
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  37. 2021Computer-Aided Diagnosis of Low Grade Endometrial Stromal Sarcoma (LGESS)cited by 2
  38. 2021Optimal input representation in neural systems at the edge of chaoscited by 2

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