arXiv (Cornell University)· 2014· Preprint
A Tutorial on Principal Component Analysis
- 2,272citations
- 2014year
Short summary
This tutorial aims to demystify Principal Component Analysis (PCA), a widely used but often poorly understood data analysis technique, by building intuition and deriving its underlying mathematics from simple concepts.
AI-generated from the title and abstract; the full text is not read.
Key points
- PCA is a widely used but often poorly understood data analysis technique.
- The tutorial focuses on building intuition for PCA's mechanisms.
- It derives the mathematics of PCA from simple intuitions.
- The paper aims to explain both the informal ideas and the formal mathematics of PCA.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. arXiv (Cornell University), 2014 · DOI ↗
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Field: Signal Processing
Signal ProcessingComputer Science