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
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)
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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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