Sparse cluster analysis of large-scale discrete variables with application to single nucleotide polymorphism data

DOI10.1080/02664763.2012.743977OpenAlexW2083466609WikidataQ30606603 ScholiaQ30606603MaRDI QIDQ5128918

Baolin Wu

Publication date: 26 October 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3601766

zbMATH Keywords

clusteringprincipal componentsexpectation-maximization algorithmlatent class model\(k\)-meanssingle nucleotide polymorphismsparse clustering

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (2)

Hybrid Hard-Soft Screening for High-dimensional Latent Class Analysis ⋮ Bayesian approaches to variable selection in mixture models with application to disease clustering

Uses Software

• glmnet
• sparcl
• clusfind

Cites Work

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• Variable Selection for Model-Based High-Dimensional Clustering and Its Application to Microarray Data
• A penalized latent class model for ordinal data
• Variable Selection in Penalized Model‐Based Clustering Via Regularization on Grouped Parameters
• The ECME algorithm: A simple extension of EM and ECM with faster monotone convergence
• Exploratory latent structure analysis using both identifiable and unidentifiable models
• A Framework for Feature Selection in Clustering
• Order Selection in Finite Mixture Models With a Nonsmooth Penalty
• Feature‐Specific Penalized Latent Class Analysis for Genomic Data

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