Mixtures of common factor analyzers for high-dimensional data with missing information

Wan-Lun Wang

Journal of Multivariate Analysis, 2013, vol. 117, issue C, 120-133

Abstract: Mixtures of common factor analyzers (MCFA), thought of as a parsimonious extension of mixture factor analyzers (MFA), have recently been developed as a novel approach to analyzing high-dimensional data, where the number of observations n is not very large relative to their dimension p. The key idea behind MCFA is to reduce further the number of parameters in the specification of the component-covariance matrices. An attractive and important feature of MCFA is to allow visualizing data in lower dimensions. The occurrence of missing data persists in many scientific investigations and often complicates data analysis. In this paper, we establish a computationally flexible EM-type algorithm for parameter estimation of the MCFA model with partially observed data. To facilitate the implementation, two auxiliary permutation matrices are incorporated into the estimating procedure for exactly extracting the location of observed and missing components of each observation. Practical issues related to the specification of initial values, model-based clustering and discriminant procedure are also discussed. Our methodology is illustrated through real and simulated examples.

Keywords: Clustering; Common factor loadings; Dimension reduction; Discriminant analysis; ECM algorithm; Mixture factor analyzers (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047259X13000171
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:jmvana:v:117:y:2013:i:c:p:120-133

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.jmva.2013.02.003

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().