Poster
Improved Graph Laplacian via Geometric Self-Consistency
Dominique Perrault-Joncas · Marina Meila · James McQueen
Pacific Ballroom #46
Keywords: [ Hyperparameter Selection ] [ Nonlinear Dimensionality Reduction and Manifold Learning ] [ Unsupervised Learning ] [ Semi-Supervised Learning ] [ Kernel Methods ]
We address the problem of setting the kernel bandwidth, epps, used by Manifold Learning algorithms to construct the graph Laplacian. Exploiting the connection between manifold geometry, represented by the Riemannian metric, and the Laplace-Beltrami operator, we set epps by optimizing the Laplacian's ability to preserve the geometry of the data. Experiments show that this principled approach is effective and robust
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