A study of the optimality of approximate maximum likelihood estimation
David McKinnon, Brian C. Lovell · 2005
Maximum Likelihood Estimation (MLE) is widely utilized in the computer vision literature as a means of solving parameter estimation problems assuming a Gaussian noise model for the measurement data. In order to solve a MLE problem it is necessary to have knowledge of the true parameters of the Gaussian noise model. Since this knowledge is unobtainable in practical setting approximate MLE has become a popular alter-native. The theory behind the approximate MLE frame-work is presented and an analysis of the bias character-istics of the method for noisy data is performed. Sev-eral experiments are performed to ascertain the opti-mality of approximate MLE solutions and to determine whether or not there is a correlation between the degree and dimension of the algebraic hypersurface and opti-mality of the error metric. 1.