A procedure based on a Mixture Density Model for correcting experimental data for distortions due to finite resolution and limited detector acceptance is presented. Addressing the case that the solution is known to be non-negative, in the approach presented here, the true distribution is estimated by a weighted sum of probability density functions with positive weights and with the width of the densities acting as a regularization parameter responsible for the smoothness of the result. To obtain better smoothing in less populated regions, the width parameter is chosen inversely proportional to the square root of the estimated density. Furthermore, the non-negative garrote method is used to find the most economic representation of the solution. Cross-validation is employed to determine the optimal values of the resolution and garrote parameters. The proposed approach is directly applicable to multidimensional problems. Numerical examples in one and two dimensions are presented to illustrate the procedure.