Dynamic movement primitives (DMPs) have been proposed as a powerful, robust and adaptive tool for planning robot trajectories based on demonstrated example movements. Adaptation of DMPs to new task requirements becomes difficult when demonstrated trajectories are only available in joint space, because their parameters do not in general correspond to variables meaningful for the task. This problem becomes more severe with increasing number of degrees of freedom and hence is particularly an issue for humanoid movements. It has been shown that DMP parameters can directly relate to task variables, when DMPs are learned in latent spaces resulting from dimensionality reduction of demonstrated trajectories. As we show here, however, standard dimensionality reduction techniques do not in general provide adequate latent spaces which need to be highly regular. In this work we concentrate on learning discrete (point-topoint) movements and propose a modification of a powerful nonlinear dimensionality reduction technique (Gaussian Process Latent Variable Model). Our modification makes the GPLVM more suitable for the use of DMPs by favouring latent spaces with highly regular structure. Even though in this case the user has to provide a structure hypothesis we show that its precise choice is not important in order to achieve good results. Additionally, we can overcome one of the main disadvantages of the GPLVM with this modification: its dependence on the initialisation of the latent space. We motivate our approach on data from a 7-DoF robotic arm and demonstrate its feasibility on a high-dimensional human motion capture data set.