Finite Multi-dimensional Generalized Gamma Mixture Model Learning Based on MML

In this paper, an unsupervised learning algorithm of a finite multi-dimensional generalized Gamma mixture model (GGMM) is presented to tackle the issue of simultaneously clustering positive vectors and determining the number of clusters. The parameters of the model are estimated using maximum likelihood (ML) which is conducted via expectation maximization (EM) [1]. Newton Raphson's optimization algorithm is also utilized to solve the issue of the non-existence of closed form. Moreover, we developed a method based on Minimum message length (MML) criterion, in order to select the optimal number of clusters which best describes the data. Furthermore, Experiments are conducted using both synthetic data and real data sets of images representing shapes and textures to test the performance of the proposed model.