08.10.2024, 10:15 - 11:45
– 3.06.H02
Kálmán Lecture
Particle Methods in Machine Learning and Inverse Problems
Martin Burger, Helmholtz Imaging
Maximilian Graf
The Laplacian Eigenmaps algorithm as a non-linear dimensionality reduction method was introduced by Belkin and Niyogi in 2001. Key objects are the Graph-Laplace-Matrix and the related Point-Cloud-Laplace-Operator, which can be seen as an approximation of the Laplacian on a manifold, the Laplace-Beltrami-Operator. I will sketch a proof of how its eigenspaces can be approximated by those of the Point-Cloud-Laplacian and how this motivates the Laplacian Eigenmaps algorithm.
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