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• On Laplacian Eigenmaps for Dimensionality Reduction

On Laplacian Eigenmaps for Dimensionality Reduction

Abstract

The aim of this talk is to describe the non-linear dimensionality reduction algorithm based on spectral techniques introduced in \cite{BN2003}. This approach has its foundation on the spectral analysis of graph Laplacian. The motivation of the construction comes from the role of the continuous limit, the Laplace-Beltrami operator, in providing an optimal embedding for manifolds.

Spectral Clustering, Laplacian Eigenmap

• Ali Ghodsi’s lecture on January 24, 2017 for STAT 442/842: Data Visualization, held at the University of Waterloo.

Laplacian Eigenmaps

• Laplacian Eigenmaps explained by Jisu Kim