2 papers
stat.ML2025
Central limit theorems for the eigenvalues of graph Laplacians on data clouds
Chenghui Li, Nicolás GarcÃa Trillos, Housen Li +1
Given i.i.d.\ samples from a distribution supported on a low dimensional manifold embedded in Eucliden space, we consider the graph Laplacian ope…
cs.DS2024
A scalable clustering algorithm to approximate graph cuts
Leo Suchan, Housen Li, Axel Munk
Due to their computational complexity, graph cuts for cluster detection and identification are used mostly in the form of convex relaxations. We propose to utilize the original gra…