Strong consistency guarantees for clustering high-dimensional bipartite graphs with the spectral method
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Publication:6595781
DOI10.1214/24-EJS2271MaRDI QIDQ6595781
Publication date: 30 August 2024
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Cites Work
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- Hanson-Wright inequality and sub-Gaussian concentration
- Subspace estimation from unbalanced and incomplete data matrices: \({\ell_{2,\infty}}\) statistical guarantees
- Optimality of spectral clustering in the Gaussian mixture model
- An \({\ell_p}\) theory of PCA and spectral clustering
- Entrywise eigenvector analysis of random matrices with low expected rank
- The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics
- Consistency of spectral clustering in stochastic block models
- Analyzing Consumer-Product Graphs: Empirical Findings and Applications in Recommender Systems
- Community Detection and Stochastic Block Models
- An $\ell_{\infty}$ Eigenvector Perturbation Bound and Its Application to Robust Covariance Estimation
- Improved Clustering Algorithms for the Bipartite Stochastic Block Model
- Uniform Bounds for Invariant Subspace Perturbations
- Strong Consistency of Spectral Clustering for Stochastic Block Models
- Spectral techniques applied to sparse random graphs
- Signal-plus-noise matrix models: eigenvector deviations and fluctuations
- Spectral recovery of binary censored block models
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