41 citations · 264 across the 40 of their papers we have counts for
7 papers · 1 filter
The Gaussian Transform
Kun Jin, Facundo Mémoli, Zhengchao Wan
We introduce the Gaussian transform (GT), an optimal transport inspired iterative method for denoising and enhancing latent structures in datasets. Under the hood, GT generates a n…
The Wasserstein transform
Kun Jin, Facundo Mémoli, Zane Smith +1
We introduce the Wasserstein Transform (WT), a general unsupervised framework for updating distance structures on given data sets with the purpose of enhancing features and denoisi…
Excisive Hierarchical Clustering Methods for Network Data
Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro +1
We introduce two practical properties of hierarchical clustering methods for (possibly asymmetric) network data: excisiveness and linear scale preservation. The latter enforces imp…
Admissible Hierarchical Clustering Methods and Algorithms for Asymmetric Networks
Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro +1
This paper characterizes hierarchical clustering methods that abide by two previously introduced axioms -- thus, denominated admissible methods -- and proposes tractable algorithms…
Hierarchical Clustering of Asymmetric Networks
Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro +1
This paper considers networks where relationships between nodes are represented by directed dissimilarities. The goal is to study methods that, based on the dissimilarity structure…
Hierarchical Quasi-Clustering Methods for Asymmetric Networks
Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro +1
This paper introduces hierarchical quasi-clustering methods, a generalization of hierarchical clustering for asymmetric networks where the output structure preserves the asymmetry…