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

Nam-Hwui Kim and Ryan P. Browne

Computational Statistics & Data Analysis, 2021, vol. 160, issue C

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.

Keywords: Model-based clustering; Parsimonious mixture model; Sparse factor analyzer; Mixture of factor analyzers (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947321000785
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:160:y:2021:i:c:s0167947321000785

DOI: 10.1016/j.csda.2021.107244

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().