NeurIPS 2017poster26 citations
Improved Graph Laplacian via Geometric Self-Consistency
Dominique Joncas, Marina Meila, James McQueen
Abstract
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
BibTeX
@inproceedings{NIPS2017_619205da,
author = {Joncas, Dominique and Meila, Marina and McQueen, James},
booktitle = {Advances in Neural Information Processing Systems},
editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Improved Graph Laplacian via Geometric Self-Consistency},
url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/619205da514e83f869515c782a328d3c-Paper.pdf},
volume = {30},
year = {2017}
}