Flexible clustering via extended mixtures of common t-factor analyzers

Wan-Lun Wang () and Tsung-I Lin ()
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Wan-Lun Wang: Feng Chia University
Tsung-I Lin: National Chung Hsing University

AStA Advances in Statistical Analysis, 2017, vol. 101, issue 3, No 1, 227-252

Abstract: Abstract Mixtures of t-factor analyzers have been broadly used for model-based density estimation and clustering of high-dimensional data from a heterogeneous population with longer-than-normal tails or atypical observations. To reduce the number of parameters in the component covariance matrices, the mixtures of common t-factor analyzers (MCtFA) have been recently proposed by assuming a common factor loading across different components. In this paper, we present an extended version of MCtFA using distinct covariance matrices for component errors. The modified mixture model offers a more appropriate way to represent the data in a graphical fashion. Two flexible EM-type algorithms are developed for iteratively computing maximum likelihood estimates of parameters. Practical considerations for the specification of starting values, model-based clustering, classification of new subject and identification of potential outliers are also provided. We demonstrate the superiority of the proposed methodology by analyzing the Italian wine data and a simulation study.

Keywords: Clustering; Classification; Factor loadings; Mixture models; Outlier detection; 62H25; 62H30 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10182-016-0281-0

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