Eigen Selection in Spectral Clustering: A Theory-Guided Practice

DOI10.1080/01621459.2021.1917418zbMath1514.62108arXiv2004.06296OpenAlexW3152746227MaRDI QIDQ6107194

Xin Tong, Yingying Fan, Xiao Han

Publication date: 3 July 2023

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2004.06296

zbMATH Keywords

clusteringeigenvalueseigenvectorsasymptotic expansionshigh dimensionalitylow-rank modelseigen selection

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Eigenvalues, singular values, and eigenvectors (15A18) Multilinear algebra, tensor calculus (15A69)

Related Items (1)

Optimal Estimation of the Number of Network Communities

Cites Work

• Fast community detection by SCORE
• Influential features PCA for high dimensional clustering
• Tightness of the maximum likelihood semidefinite relaxation for angular synchronization
• Entrywise eigenvector analysis of random matrices with low expected rank
• CHIME: clustering of high-dimensional Gaussian mixtures with EM algorithm and its optimality
• Sparse PCA: optimal rates and adaptive estimation
• Spectral clustering with eigenvector selection
• Singular vector and singular subspace distribution for the matrix denoising model
• Mathematical Programming for Data Mining: Formulations and Challenges
• A Framework for Feature Selection in Clustering
• Using Evidence of Mixed Populations to Select Variables for Clustering Very High-Dimensional Data
• Asymptotic Theory of Eigenvectors for Random Matrices With Diverging Spikes

This page was built for publication: Eigen Selection in Spectral Clustering: A Theory-Guided Practice