2013/03/10 by Tsung‐I Lin, Paul D. McNicholas, Lin, Tsung-I +3
Computer Science · Mathematics · #Algorithms and Data Compression #Applications (stat.AP) #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.1303.2316
openalex publication_date 2013/03/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper exploits a simplified version of the mixture of multivariate t-factor analyzers (MtFA) for robust mixture modelling and clustering of high-dimensional data that frequently contain a number of outliers. Two classes of eight parsimonious t mixture models are introduced and computation of maximum likelihood estimates of parameters is achieved using the alternating expectation conditional maximization (AECM) algorithm. The usefulness of the methodology is illustrated through applications of image compression and compact facial representation.