Extending mixtures of multivariate <Emphasis Type="Italic">t</Emphasis>-factor analyzers |
| |
Authors: | Jeffrey L Andrews Paul D McNicholas |
| |
Institution: | (1) College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing, 210016, China;(2) Departments of Biological Chemistry and Medicine, Institute for Genomics and Bioinformatics, University of California, Irvine, CA 92697, USA;(3) School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, Manchester, M13 9PL, UK |
| |
Abstract: | Model-based clustering typically involves the development of a family of mixture models and the imposition of these models
upon data. The best member of the family is then chosen using some criterion and the associated parameter estimates lead to
predicted group memberships, or clusterings. This paper describes the extension of the mixtures of multivariate t-factor analyzers model to include constraints on the degrees of freedom, the factor loadings, and the error variance matrices.
The result is a family of six mixture models, including parsimonious models. Parameter estimates for this family of models
are derived using an alternating expectation-conditional maximization algorithm and convergence is determined based on Aitken’s
acceleration. Model selection is carried out using the Bayesian information criterion (BIC) and the integrated completed likelihood
(ICL). This novel family of mixture models is then applied to simulated and real data where clustering performance meets or
exceeds that of established model-based clustering methods. The simulation studies include a comparison of the BIC and the
ICL as model selection techniques for this novel family of models. Application to simulated data with larger dimensionality
is also explored. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|