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

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  • Kim, Nam-Hwui
  • Browne, Ryan P.

Abstract

A finite mixture of factor analyzers is an effective method for achieving parsimony in model-based clustering. Introducing a penalization term for the factor loading can lead to sparse estimates. However, in the pursuit of sparseness, one can end up with rank-deficient solutions regardless of the number of factors assumed. In light of this issue, a new penalty-based method that can fit a finite mixture of sparse factor analyzers with full-rank factor loading estimates is developed. In addition, the extension of an existing penalized factor analyzer model to a finite mixture is introduced.

Suggested Citation

  • Kim, Nam-Hwui & Browne, Ryan P., 2021. "In the pursuit of sparseness: A new rank-preserving penalty for a finite mixture of factor analyzers," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    • Handle: RePEc:eee:csdana:v:160:y:2021:i:c:s0167947321000785
    DOI: 10.1016/j.csda.2021.107244
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