7.6k citations · 8.1k across the 7 of their papers we have counts for
4 papers · 1 filter
Persistent Multiscale Density-based Clustering
Daniël Bot, Leland McInnes, Jan Aerts
Clustering is a cornerstone of modern data analysis. Detecting clusters in exploratory data analyses (EDA) requires algorithms that make few assumptions about the data. Density-bas…
Low-dimensional embeddings of high-dimensional data
Cyril de Bodt, Alex Diaz-Papkovich, Michael Bleher +18
Large collections of high-dimensional data have become nearly ubiquitous across many academic fields and application domains, ranging from biology to the humanities. Since working…
Improving Mapper's Robustness by Varying Resolution According to Lens-Space Density
Kaleb D. Ruscitti, Leland McInnes
We propose a modification of the Mapper algorithm that removes the assumption of a single resolution scale across semantic space and improves the robustness of the results under ch…
Parametric UMAP embeddings for representation and semi-supervised learning
Tim Sainburg, Leland McInnes, Timothy Q Gentner
UMAP is a non-parametric graph-based dimensionality reduction algorithm using applied Riemannian geometry and algebraic topology to find low-dimensional embeddings of structured da…