arXiv Open Access 2014

Improved graph Laplacian via geometric self-consistency

Dominique Perrault-Joncas Marina Meila

Lihat Sumber

Abstrak

We address the problem of setting the kernel bandwidth 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 the bandwidth by optimizing the Laplacian's ability to preserve the geometry of the data. Experiments show that this principled approach is effective and robust.

Topik & Kata Kunci

stat.ML cs.LG

Penulis (2)

Dominique Perrault-Joncas

Marina Meila

Format Sitasi

APA MLA BibTeX

Perrault-Joncas, D., Meila, M. (2014). Improved graph Laplacian via geometric self-consistency. https://arxiv.org/abs/1406.0118

Akses Cepat

Lihat di Sumber

Informasi Jurnal

Tahun Terbit: 2014
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓