ArticleElectronic journal of statistics2008
Penalized model-based clustering with cluster-specific diagonal covariance matrices and grouped variables.
Article in Electronic journal of statistics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Sparse kernelJournal of applied statistics · 2025Article
- Outcome-guided Bayesian clustering for disease subtype discovery using high-dimensional transcriptomic data.Journal of applied statistics · 2025Article
- Past mercury exposure and current symptoms of nervous system dysfunction in adults of a First Nation community (Canada).Environmental health : a global access science source · 2022Article
- Discovering a sparse set of pairwise discriminating features in high-dimensional data.Bioinformatics (Oxford, England) · 2021Article
- Integrative SparseThe annals of applied statistics · 2017Article
- Meta-analytic framework for sparseJournal of the American Statistical Association · 2016Article
- Estimation of multiple networks in Gaussian mixture models.Electronic journal of statistics · 2016Article
- Comparing Model Selection and Regularization Approaches to Variable Selection in Model-Based Clustering.Journal de la Societe francaise de statistique (2009) · 2014Article
- Sparse Biclustering of Transposable Data.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2014Article
- Cluster Analysis: Unsupervised Learning via Supervised Learning with a Non-convex Penalty.Journal of machine learning research : JMLR · 2013Article
- Penalized unsupervised learning with outliers.Statistics and its interface · 2013Article
- Cancer subtype discovery and biomarker identification via a new robust network clustering algorithm.PloS one · 2013Article
- Article
- Cluster analysis for identifying sub-groups and selecting potential discriminatory variables in human encephalitis.BMC infectious diseases · 2010Article
- Article
- A framework for feature selection in clustering.Journal of the American Statistical Association · 2010Article
- Penalized mixtures of factor analyzers with application to clustering high-dimensional microarray data.Bioinformatics (Oxford, England) · 2010Article
- RCMAT: a regularized covariance matrix approach to testing gene sets.BMC bioinformatics · 2009Article
- Penalized model-based clustering with unconstrained covariance matrices.Electronic journal of statistics · 2009Article
- Statistical Significance of Clustering using Soft Thresholding.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North AmericaArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Clustering analysis is one of the most widely used statistical tools in many emerging areas such as microarray data analysis. For microarray and other high-dimensional data, the presence of many noise variables may mask underlying clustering structures. Hence removing noise variables via variable selection is necessary. For simultaneous variable selection and parameter estimation, existing penalized likelihood approaches in model-based clustering analysis all assume a common diagonal covariance matrix across clusters, which however may not hold in practice. To analyze high-dimensional data, particularly those with relatively low sample sizes, this article introduces a novel approach that shrinks the variances together with means, in a more general situation with cluster-specific (diagonal) covariance matrices. Furthermore, selection of grouped variables via inclusion or exclusion of a group of variables altogether is permitted by a specific form of penalty, which facilitates incorporating subject-matter knowledge, such as gene functions in clustering microarray samples for disease subtype discovery. For implementation, EM algorithms are derived for parameter estimation, in which the M-steps clearly demonstrate the effects of shrinkage and thresholding. Numerical examples, including an application to acute leukemia subtype discovery with microarray gene expression data, are provided to demonstrate the utility and advantage 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.