However, the goal of DA is to improve the optimization of a non-concave objective function, which is achieved by setting the exponent in the E-step to a value close to 0, so that the distribution of the hidden variables becomes nearly uniform and the objective function becomes almost concave and therefore easy to optimize; this exponent is then gradually increased to 1 to optimize the original objective function.
here we incorporate unambiguity regularization into mean-field variational inference.