I Use This When...
<!-- Practical use case -->History
Laurens van der Maaten & Geoffrey Hinton (2008). Designed specifically for visualizing high-dimensional data in 2D/3D.
Why It Exists
PCA preserves global structure but often fails to show local clusters. t-SNE preserves local neighborhoods — similar points stay close.
How It Works
Visual Intuition
<!-- 3B1B-style animation description -->Step by Step
<!-- Algorithm walkthrough -->Code
# Implementation sketch
The Math Inside
Convert distances to probabilities in high-D (Gaussian) and low-D (t-distribution). Minimize KL divergence between the two distributions via gradient descent.
Math Prerequisites
<!-- Links to math wiki -->Related
- PCA — Linear alternative
- UMAP — Faster alternative
- KL Divergence — What t-SNE minimizes