The geometry of kernelized spectral clustering

DOI10.1214/14-AOS1283zbMath1312.62082arXiv1404.7552OpenAlexW2032519339MaRDI QIDQ2343965

Martin J. Wainwright, Bin Yu, Geoffrey Schiebinger

Publication date: 11 May 2015

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1404.7552

zbMATH Keywords

kernel functionmixture modelnormalized Laplacianspectral clustering

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items

Balancing Geometry and Density: Path Distances on High-Dimensional Data, Clustering Dynamics on Graphs: From Spectral Clustering to Mean Shift Through Fokker–Planck Interpolation, Unnamed Item, Unnamed Item, Concentration of kernel matrices with application to kernel spectral clustering, Diffusion \(K\)-means clustering on manifolds: provable exact recovery via semidefinite relaxations, Spectral analysis of weighted Laplacians arising in data clustering, Unnamed Item, Identifiability of nonparametric mixture models and Bayes optimal clustering, Spectral clustering via sparse graph structure learning with application to proteomic signaling networks in cancer, Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian, Unnamed Item, A multiscale environment for learning by diffusion, The geometry of kernelized spectral clustering, A mixture model approach to spectral clustering and application to textual data

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Pseudo-likelihood methods for community detection in large sparse networks
• Consistency of regularized spectral clustering
• Data spectroscopy: eigenspaces of convolution operators and clustering
• Cluster forests
• The geometry of kernelized spectral clustering
• Consistency of spectral clustering
• Bounds on the L 2 Spectrum for Markov Chains and Markov Processes: A Generalization of Cheeger's Inequality
• Empirical graph Laplacian approximation of Laplace–Beltrami operators: Large sample results
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• Error Bounds for Approximate Invariant Subspaces of Closed Linear Operators
• Lower Bounds for the Partitioning of Graphs