An efficient ECM algorithm for maximum likelihood estimation in mixtures of t-factor analyzers

作者：Wang Wan Lun; Lin Tsung I^*

来源：Computational Statistics, 2013, 28(2): 751-769.

DOI：10.1007/s00180-012-0327-z

• AECM algorithm
• ECM algorithm
• EM algorithm
• Maximum likelihood estimation
• MFA
• MtFA

Mixture of t factor analyzers (MtFA) have been shown to be a sound model-based tool for robust clustering of high-dimensional data. This approach, which is deemed to be one of natural parametric extensions with respect to normal-theory models, allows for accommodation of potential noise components, atypical observations or data with longer-than-normal tails. In this paper, we propose an efficient expectation conditional maximization (ECM) algorithm for fast maximum likelihood estimation of MtFA. The proposed algorithm inherits all appealing properties of the ordinary EM algorithm such as its stability and monotonicity, but has a faster convergence rate since its CM steps are governed by a much smaller fraction of missing information. Numerical experiments based on simulated and real data show that the new procedure outperforms the commonly used EM and AECM algorithms substantially in most of the situations, regardless of how the convergence speed is assessed by the computing time or number of iterations.

• 出版日期2013-4
• 单位中国医科大学