5 citations · 7 across the 2 of their papers we have counts for
2 papers
stat.ML2014★ 2 cited
Improved graph Laplacian via geometric self-consistency
Dominique Perrault-Joncas, Marina Meila
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, r…
stat.ML2014★ 5 cited
Estimating Vector Fields on Manifolds and the Embedding of Directed Graphs
Dominique Perrault-Joncas, Marina Meila
This paper considers the problem of embedding directed graphs in Euclidean space while retaining directional information. We model a directed graph as a finite set of observations…