Multivariate bounded support asymmetric generalized Gaussian mixture model with model selection using minimum message length. (15th October 2022) - Azam, Muhammad; Bouguila, Nizar
Abstract: In this paper, the bounded support asymmetric generalized Gaussian mixture model (BAGGMM) is proposed for data modeling as an alternative to unbounded mixture models for the cases when the data lies in the bounded support region. The model parameters are learned through maximum likelihood estimation, and Expectation–Maximization (EM) with the Newton Raphson method is adopted for optimization of parameters. Model selection in mixtures is also considered an integral part of clustering; thus, we also have proposed a model selection criterion for BAGGMM through minimum message length. In order to validate the performance of the mixture model, it is applied to image spam detection, object clustering, and visual scene categorization. For the experiments, Spam Hunter, ETHZ, GHIM, and 15-Scene image datasets are adopted, and several clustering scenarios are developed to see the effectiveness of the proposed model. The clustering framework is also compared with AGGMM in a similar setting to all the experiments. In the next phase, the whole clustering framework is extended to examine the performance of the proposed model selection criterion and compared with different techniques to find the optimal mixture component, which further improves the clustering process. The experiments show that the proposed BAGGMM and model selection criterion has demonstrated its success in several learning applications. Highlights: Bounded support asymmetric generalized Gaussian mixture model (BAGGMM) is proposed. Parameters estimation is performed through ML and EM with Newton Raphson algorithm. Model is validated via image spam detection, object & visual scene categorization. Model selection criterion for BAGGMM using Minimum Message Length (MML) is proposed. Effectiveness of MML in proposed BAGGMM is tested via above mentioned applications.