Balancing Geometry and Density: Path Distances on High-Dimensional Data

DOI10.1137/20M1386657zbMath1493.62391arXiv2012.09385OpenAlexW4226462355MaRDI QIDQ5037564

James M. Murphy, Daniel McKenzie, Anna V. Little

Publication date: 1 March 2022

Published in: SIAM Journal on Mathematics of Data Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2012.09385

zbMATH Keywords

clusteringkernel methodsmachine learninghigh-dimensional statisticsmanifold learningpath distances

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Computational aspects of data analysis and big data (68T09)

Related Items

Clustering Dynamics on Graphs: From Spectral Clustering to Mean Shift Through Fokker–Planck Interpolation ⋮ Geometric scattering on measure spaces

Uses Software

• t-SNE

Cites Work

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