Model-based clustering with sparse covariance matrices
From MaRDI portal
Publication:2329799
DOI10.1007/s11222-018-9838-yzbMath1430.62131arXiv1711.07748OpenAlexW2756395418MaRDI QIDQ2329799
Thomas Brendan Murphy, Luca Scrucca, Michael Fop
Publication date: 18 October 2019
Published in: Statistics and Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1711.07748
model-based clusteringgenetic algorithmGaussian graphical modelspenalized likelihoodfinite Gaussian mixture modelssparse covariance matricesstepwise searchstructural-EM algorithm
Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30)
Related Items
Group-wise shrinkage estimation in penalized model-based clustering ⋮ Clustered sparse structural equation modeling for heterogeneous data ⋮ High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood ⋮ Gaussian mixture model with an extended ultrametric covariance structure
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Model-based clustering based on sparse finite Gaussian mixtures
- Sparse estimation of a covariance matrix
- Sparse inverse covariance estimation with the graphical lasso
- Simultaneous model-based clustering and visualization in the Fisher discriminative subspace
- Estimation of multiple networks in Gaussian mixture models
- Stable and visualizable Gaussian parsimonious clustering models
- Scaling it up: stochastic search structure learning in graphical models
- Covariance estimation: the GLM and regularization perspectives
- Bayesian sparse graphical models for classification with application to protein expression data
- Sparse Gaussian graphical mixture model
- Genetic algorithms and their statistical applications: an introduction
- Using conditional independence for parsimonious model-based Gaussian clustering
- Covariance chains
- Technological modelling for graphical models: an approach based on genetic algorithms
- Bayesian regularization for normal mixture estimation and model-based clustering
- On the convergence properties of the EM algorithm
- Estimating the dimension of a model
- Bayesian model averaging: A tutorial. (with comments and a rejoinder).
- Model-based clustering of high-dimensional data: a review
- Modelling the role of variables in model-based cluster analysis
- A macro-DAG structure based mixture model
- Variable selection methods for model-based clustering
- Ancestral graph Markov models.
- Penalized model-based clustering with unconstrained covariance matrices
- Sparse covariance estimation in heterogeneous samples
- High-dimensional Ising model selection with Bayesian information criteria
- Improved initialisation of model-based clustering using Gaussian hierarchical partitions
- On the ``degrees of freedom of the lasso
- Finite mixture and Markov switching models.
- Model-based clustering
- Positive definite estimators of large covariance matrices
- Node-Based Learning of Multiple Gaussian Graphical Models
- Regularized Parameter Estimation in High-Dimensional Gaussian Mixture Models
- Joint estimation of multiple graphical models
- Extended Bayesian information criteria for model selection with large model spaces
- Variable Selection for Clustering with Gaussian Mixture Models
- Model selection and estimation in the Gaussian graphical model
- Estimation of a covariance matrix with zeros
- Variable Selection in Penalized Model‐Based Clustering Via Regularization on Grouped Parameters
- Model-Based Gaussian and Non-Gaussian Clustering
- Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window
- Hyper Inverse Wishart Distribution for Non-decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models
- Penalized Maximum Likelihood Estimator for Normal Mixtures
- Adaptive Model Selection
- Model-Based Clustering, Discriminant Analysis, and Density Estimation
- Introduction to Graphical Modelling
- Convergence Criteria for Genetic Algorithms
- Structural Pursuit Over Multiple Undirected Graphs
- The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
- A genetic algorithm for graphical model selection
- Bayesian Inference of Multiple Gaussian Graphical Models
- High-dimensional covariance estimation based on Gaussian graphical models
- Covariance matrix selection and estimation via penalised normal likelihood
- Variable Selection for Model-Based Clustering
- EM for mixtures
