Mixtures of modified t-factor analyzers for model-based clustering, classification, and discriminant analysis

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• 作者：Jeffrey L. Andrews ; Paul D. McNicholas
• 关键词：Classification ; Clustering ; Discriminant analysis ; Mixture models ; Model-based ; Modified factor analysis ; Modified t-factor analyzers ; Modified t-factors ; t-Factor analyzers ; t-Factors
• 刊名：Journal of Statistical Planning and Inference
• 出版年：2011
• 出版时间：April 2011
• 年：2011
• 卷：141
• 期：4
• 页码：1479-1486
• 全文大小：187 K

文摘

A novel family of mixture models is introduced based on modified t-factor analyzers. Modified factor analyzers were recently introduced within the Gaussian context and our work presents a more flexible and robust alternative. We introduce a family of mixtures of modified t-factor analyzers that uses this generalized version of the factor analysis covariance structure. We apply this family within three paradigms: model-based clustering; model-based classification; and model-based discriminant analysis. In addition, we apply the recently published Gaussian analogue to this family under the model-based classification and discriminant analysis paradigms for the first time. Parameter estimation is carried out within the alternating expectation-conditional maximization framework and the Bayesian information criterion is used for model selection. Two real data sets are used to compare our approach to other popular model-based approaches; in these comparisons, the chosen mixtures of modified t-factor analyzers model performs favourably. We conclude with a summary and suggestions for future work.