PCA
Domain: AI Fundamentals
🎯 Motivation
Principal Component Analysis finds the directions of most variance and projects data onto them.
📋 Concrete Example
PCA on 3D data might find that most variation lies on a flat 2D plane, so you can plot it in 2D.
🔗 Analogy
Like photographing a 3D sculpture from the angle that shows the most detail - you capture the essence in 2D.