Leave-one-out singular subspace perturbation analysis for spectral clustering
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Publication:6656609
DOI10.1214/24-aos2418MaRDI QIDQ6656609
Anderson Ye Zhang, Harrison Y. Zhou
Publication date: 3 January 2025
Published in: The Annals of Statistics (Search for Journal in Brave)
Cites Work
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- Estimating the Number of Clusters in a Data Set Via the Gap Statistic
- Fast community detection by SCORE
- Asymptotics and concentration bounds for bilinear forms of spectral projectors of sample covariance
- Spectral clustering and the high-dimensional stochastic blockmodel
- Spectral analysis of large dimensional random matrices
- Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data
- Rate-optimal perturbation bounds for singular subspaces with applications to high-dimensional statistics
- Subspace estimation from unbalanced and incomplete data matrices: \({\ell_{2,\infty}}\) statistical guarantees
- Optimality of spectral clustering in the Gaussian mixture model
- Sharp optimal recovery in the two component Gaussian mixture model
- An \({\ell_p}\) theory of PCA and spectral clustering
- Entrywise eigenvector analysis of random matrices with low expected rank
- Partial recovery bounds for clustering with the relaxed \(K\)-means
- The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics
- Consistency of spectral clustering in stochastic block models
- The geometry of kernelized spectral clustering
- Consistency of spectral clustering
- Perturbation of Linear Forms of Singular Vectors Under Gaussian Noise
- Consistent selection of the number of clusters via crossvalidation
- Spectral Algorithms
- Asymptotic Statistics
- An $\ell_{\infty}$ Eigenvector Perturbation Bound and Its Application to Robust Covariance Estimation
- Analysis of spectral clustering algorithms for community detection: the general bipartite setting
- Improved Clustering Algorithms for the Bipartite Stochastic Block Model
- Entrywise Estimation of Singular Vectors of Low-Rank Matrices With Heteroskedasticity and Dependence
- Leave-One-Out Approach for Matrix Completion: Primal and Dual Analysis
- A useful variant of the Davis–Kahan theorem for statisticians
- Separating Populations with Wide Data: A Spectral Analysis
- The Rotation of Eigenvectors by a Perturbation. III
- Perturbation bounds in connection with singular value decomposition
- Exact Clustering in Tensor Block Model: Statistical Optimality and Computational Limit
- Bias-Adjusted Spectral Clustering in Multi-Layer Stochastic Block Models
- A Robust Spectral Clustering Algorithm for Sub-Gaussian Mixture Models with Outliers
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