Lecture

Spectral Clustering and Laplacian Eigenmaps

Description

This lecture covers advanced machine learning techniques focusing on spectral clustering and Laplacian eigenmaps. Spectral methods decompose the Graph Laplacian matrix to identify non-linear manifolds in data. By constructing a similarity graph and measuring distances, spectral clustering can determine the number of clusters in a dataset. The eigenvalue decomposition of the Laplacian matrix plays a crucial role in identifying connected components and clustering. Laplacian eigenmaps and Isomap are discussed as non-linear embedding methods, highlighting the importance of geodesic distances in capturing data relationships. The lecture concludes with a summary of spectral methods, emphasizing the power of spectral clustering and Laplacian embeddings when the kernel is well chosen.

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