Abstract Gaussian Mixture Models are a powerful tool in Data Science and Statistics that mainly used for clustering density approximation. The task of estimating the model parameters is practice often solved by expectation maximization (EM) algorithm which has its benefits simplicity low per-iteration costs. However, EM converges slowly if there large share hidden information or overlapping clu...

In this paper we compare the accuracy of a range of advanced density models for gas identification from sensor array signals. Density estimation is applied in the construction of classifiers through the use of Bayes rule. Experiments on real sensors data proved the effectiveness of the approach with an excellent classification performance. We compare the classification accuracy of four density ...

We propose a new intelligent blind source separation algorithm for the mixture of sub-Gaussian and superGaussian sources. The algorithm consists of an update equation of the separating matrix and an adjustment equation of nonlinear functions. To verify the validity of the proposed algorithm, we compare the proposed algorithm with extant methods. key words: blind source separation, sub-Gaussian,...

In this paper, a mixture model is estimated by the empirical likelihood (EL) approach. The functional delta method is implemented to exploit asymptotic properties of the estimator. EL constructs a weighted empirical process that converges to a Gaussian process and its estimator, with additional assumptions, converges a normal distribution with an asymptotic efficient covariance matrix. This app...

In this paper, we introduce a mixture of skew-t factor analyzers as well as a family of mixture models based thereon. The mixture of skew-t distributions model that we use arises as a limiting case of the mixture of generalized hyperbolic distributions. Like their Gaussian and t-distribution analogues, our mixture of skew-t factor analyzers are very well-suited to the model-based clustering of ...