On Laplacian Eigenmaps for Dimensionality Reduction


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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. Slides: https://juanitorduz.github.io/documents/orduz_pydata2018.pdf --- www.pydata.org

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