1. A method comprising: inputting training data, the training data comprising data for pattern recognition processing; training an initial objective function having non-Gaussian parameters, the training comprising transforming the initial objective function into, an auxiliary function of parameters that is optimizable computationally more efficiently than the initial objective function; obtaining parameters that are optimum for the auxiliary function; obtaining updated parameters by taking a weighted sum of the optimum parameters of the auxiliary function and initial model parameters; and providing the updated parameters to a pattern recognition system.