In the pursuit of sparseness: a new rank-preserving penalty for a finite mixture of factor analyzers

DOI10.1016/j.csda.2021.107244OpenAlexW3154627384MaRDI QIDQ2242013

Nam-Hwui Kim, Ryan P. Browne

Publication date: 9 November 2021

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2021.107244

zbMATH Keywords

model-based clusteringmixture of factor analyzersparsimonious mixture modelsparse factor analyzer

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Anderson relaxation test for intrinsic dimension selection in model-based clustering

Uses Software

• mclust
• teigen
• PGMM
• HDclassif
• qgraph
• mixture
• MixSAL

Cites Work

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