Data space — colored by spectral cluster assignment
k-NN graph — edge weight = Gaussian similarity
Laplacian eigenvectors (Fiedler vector + next)
Spectral embedding (2D eigenvector space)
Settings
Algorithm:
1. Build k-NN graph W
2. Compute L_norm = D^(-½)(D−W)D^(-½)
3. Find k smallest eigenvectors
4. k-means on rows of eigenvector matrix
Key insight: Fiedler vector (λ₂) captures connectivity — sign gives 2-way cut.
1. Build k-NN graph W
2. Compute L_norm = D^(-½)(D−W)D^(-½)
3. Find k smallest eigenvectors
4. k-means on rows of eigenvector matrix
Key insight: Fiedler vector (λ₂) captures connectivity — sign gives 2-way cut.