Definition
Plain language
A way of finding the few directions that explain most of the variation in a bunch of high-dimensional data.
As stated in the literature
Principal Component Analysis, a linear technique that finds orthogonal directions of maximum variance in a dataset.
Why it matters: Reducing high-dimensional data to a few meaningful axes is the workhorse first step for visualization, denoising, and feature analysis.
For example, PCA on faces might reveal that the largest source of variation across images is overall lighting.
Heard on the show
“Although a methodological skeptic would point out that PCA always gives you a top two axes.”Episode 006 — What Happens Inside Claude When It Decides to Blackmail Someone