Clustering Dynamics on Graphs: From Spectral Clustering to Mean Shift Through Fokker–Planck Interpolation
From MaRDI portal
Publication:5054577
DOI10.1007/978-3-030-93302-9_4OpenAlexW3194396107MaRDI QIDQ5054577
Nicolás García Trillos, Katy Craig, Dejan Slepčev
Publication date: 29 November 2022
Published in: Active Particles, Volume 3 (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2108.08687
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Estimating the Number of Clusters in a Data Set Via the Gap Statistic
- Scikit
- Gradient flows of the entropy for finite Markov chains
- A variational approach to the consistency of spectral clustering
- Supersymmetry and Morse theory
- Spectral convergence of graph Laplacian and heat kernel reconstruction in \(L^\infty\) from random samples
- About small eigenvalues of the Witten Laplacian
- A graph discretization of the Laplace-Beltrami operator
- Nonlocal-interaction equation on graphs: gradient flow structure and continuum limit
- Improved spectral convergence rates for graph Laplacians on \(\varepsilon \)-graphs and \(k\)-NN graphs
- Error estimates for spectral convergence of the graph Laplacian on random geometric graphs toward the Laplace-Beltrami operator
- The geometry of kernelized spectral clustering
- A survey of outlier detection methodologies
- Entropy dissipation semi-discretization schemes for Fokker-Planck equations
- Consistency of spectral clustering
- Diffusion maps
- Diffusion maps, spectral clustering and reaction coordinates of dynamical systems
- From graph to manifold Laplacian: the convergence rate
- Empirical graph Laplacian approximation of Laplace–Beltrami operators: Large sample results
- The estimation of the gradient of a density function, with applications in pattern recognition
- A Graph-Theoretic Approach to Nonparametric Cluster Analysis
- Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
- Spectral Convergence of Diffusion Maps: Improved Error Bounds and an Alternative Normalization
- Balancing Geometry and Density: Path Distances on High-Dimensional Data
- Lipschitz Regularity of Graph Laplacians on Random Data Clouds
- Convergence of graph Laplacian with kNN self-tuned kernels
- Learning Theory
- Learning Theory
This page was built for publication: Clustering Dynamics on Graphs: From Spectral Clustering to Mean Shift Through Fokker–Planck Interpolation