t-SNE (t-Distributed Stochastic Neighbor Embedding)

Dimensionality reduction: Visualize 64-dimensional handwritten digits in 2D/3D

Speed:iter/step
Max Iters:

Datasets

📊 300 handwritten digit samples

📐 64 dimensions (8×8 pixels)

🏷️ 10 categories (digits 0-9)

Each point represents an 8×8 grayscale image. t-SNE maps 64D vectors to 2D/3D preserving local neighborhoods.

Perplexity: 30

Balances local vs global structure. Low values (5-15) emphasize local clusters; high values (30-50) reveal global relationships. Typical: 30.

Learning Rate: 200

Gradient descent step size. Too low: slow convergence. Too high: unstable. Range: 10-1000. Typical: 100-500.

Algorithm Phases

Early Exag.First 250 iterations amplify distances to separate global clusters
OptimizationRemaining iterations refine local structure while maintaining global layout
CompleteSimilar digits cluster together; dissimilar digits separate

What to Look For

Digit clustering: Similar digits (e.g., 3s, 8s) naturally group together.

Confusion zones: Look for overlaps between similar digits like 4/9 or 3/8.

Handwriting variation: Each digit cluster shows natural writing style diversity.

Tip: Try different perplexity values to see how local and global structure trade off.

Understanding t-SNE