Spectral clustering via sparse graph structure learning with application to proteomic signaling networks in cancer

DOI10.1016/j.csda.2018.08.009OpenAlexW2888185562WikidataQ129354905 ScholiaQ129354905MaRDI QIDQ1727851

Veerabhadran Baladandayuthapani, Sayantan Banerjee, Rehan Akbani

Publication date: 21 February 2019

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2018.08.009

zbMATH Keywords

spectral clusteringgraph clusteringgraph structure learningproteomic data

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Estimation in multivariate analysis (62H12) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (2)

Graph signal denoising using \(t\)-shrinkage priors ⋮ Special issue on biostatistics

Uses Software

• glasso

Cites Work

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• Sparse inverse covariance estimation with the graphical lasso
• A well-conditioned estimator for large-dimensional covariance matrices
• Bayesian sparse graphical models for classification with application to protein expression data
• Spectral clustering and the high-dimensional stochastic blockmodel
• Covariance regularization by thresholding
• Operator norm consistent estimation of large-dimensional sparse covariance matrices
• Nonparametric Bayesian inference in applications
• More nonparametric Bayesian inference in applications
• Sparse permutation invariant covariance estimation
• Network exploration via the adaptive LASSO and SCAD penalties
• The geometry of kernelized spectral clustering
• Regularized estimation of large covariance matrices
• Diffusion maps
• High-dimensional graphs and variable selection with the Lasso
• Bayesian graphical Lasso models and efficient posterior computation
• A Constrainedℓ₁Minimization Approach to Sparse Precision Matrix Estimation
• Joint estimation of multiple graphical models
• Model selection and estimation in the Gaussian graphical model
• Graph Wavelets for Multiscale Community Mining
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• Partial Correlation Estimation by Joint Sparse Regression Models
• Generalized Thresholding of Large Covariance Matrices
• A useful variant of the Davis–Kahan theorem for statisticians
• Covariance matrix selection and estimation via penalised normal likelihood
• The Rotation of Eigenvectors by a Perturbation. III
• Lower Bounds for the Partitioning of Graphs
• Detecting differential gene expression with a semiparametric hierarchical mixture method

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