Recognition of a Mixture of Multiple Gaussian Patterns (original) (raw)

In this paper a methodology for the recognition of multiple Gaussian patterns by estimating sufficient parameters of a finite mixture model (FMM) is proposed. Regular methods of FMM identification require initial guess values (IGVs) that may result in high computation time, slow convergence and or even fail to converge if the provided IGVs are far from the optimal solution. The FMM is firstly decomposed into it's even and odd parts, which are linearised through differential techniques. Secondly the ordinary least squares (OLS) method is employed to estimate the unknown parameters in the linearised models. A Monte Carlo simulation is done to evaluate the performance of the proposed method (PM). It is shown that numerical results of the PM compare well with the simulated values. The study indicates that (i) the PM can be used symbiotically with the regular methods to compute IGVs; and (ii) can be used to estimate a general n-component Gaussian model.