What is spectral clustering and how is it related to dimensionality reduction?

Spectral clustering uses eigenvectors of a graph Laplacian as a low-dimensional embedding.

It constructs a similarity graph, computes the Laplacian matrix, and uses its eigenvectors to embed data into a lower-dimensional space where clustering becomes easier. This embedding step is effectively a dimensionality reduction process.