ArticleElectronic journal of statistics2009
Penalized model-based clustering with unconstrained covariance matrices.
Article in Electronic journal of statistics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Simultaneous clustering and estimation of networks in multiple graphical models.Biostatistics (Oxford, England) · 2025Article
- Unveiling non-small cell lung cancer treatment effect heterogeneity: a comparative analysis of statistical methods.Journal of the National Cancer Institute · 2025Article
- Sparse kernelJournal of applied statistics · 2025Article
- OUTCOME-GUIDED DISEASE SUBTYPING BY GENERATIVE MODEL AND WEIGHTED JOINT LIKELIHOOD IN TRANSCRIPTOMIC APPLICATIONS.The annals of applied statistics · 2024Article
- Avoiding inferior clusterings with misspecified Gaussian mixture models.Scientific reports · 2023Article
- Simultaneous cluster structure learning and estimation of heterogeneous graphs for matrix-variate fMRI data.Biometrics · 2023Article
- A sparse negative binomial mixture model for clustering RNA-seq count data.Biostatistics (Oxford, England) · 2022Article
- Penalized model-based clustering of fMRI data.Biostatistics (Oxford, England) · 2022Article
- A Penalized Matrix Normal Mixture Model for Clustering Matrix Data.Entropy (Basel, Switzerland) · 2021Article
- Graph-based sparse linear discriminant analysis for high-dimensional classification.Journal of multivariate analysis · 2019Article
- Simultaneous Clustering and Estimation of Heterogeneous Graphical Models.Journal of machine learning research : JMLR · 2018Article
- Molecular heterogeneity at the network level: high-dimensional testing, clustering and a TCGA case study.Bioinformatics (Oxford, England) · 2017Article
- Estimation of multiple networks in Gaussian mixture models.Electronic journal of statistics · 2016Article
- Clustering High-Dimensional Landmark-based Two-dimensional Shape DataJournal of the American Statistical Association · 2015Article
- Comparing Model Selection and Regularization Approaches to Variable Selection in Model-Based Clustering.Journal de la Societe francaise de statistique (2009) · 2014Article
- Cancer subtype discovery and biomarker identification via a new robust network clustering algorithm.PloS one · 2013Article
- Penalized mixtures of factor analyzers with application to clustering high-dimensional microarray data.Bioinformatics (Oxford, England) · 2010Article
- Integrative clustering methods for multi-omics data.Wiley interdisciplinary reviews. Computational statisticsArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Clustering is one of the most useful tools for high-dimensional analysis, e.g., for microarray data. It becomes challenging in presence of a large number of noise variables, which may mask underlying clustering structures. Therefore, noise removal through variable selection is necessary. One effective way is regularization for simultaneous parameter estimation and variable selection in model-based clustering. However, existing methods focus on regularizing the mean parameters representing centers of clusters, ignoring dependencies among variables within clusters, leading to incorrect orientations or shapes of the resulting clusters. In this article, we propose a regularized Gaussian mixture model permitting a treatment of general covariance matrices, taking various dependencies into account. At the same time, this approach shrinks the means and covariance matrices, achieving better clustering and variable selection. To overcome one technical challenge in estimating possibly large covariance matrices, we derive an E-M algorithm utilizing the graphical lasso (Friedman et al 2007) for parameter estimation. Numerical examples, including applications to microarray gene expression data, demonstrate the utility of the proposed method.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.