  We propose a novel model for nonlinear dimension reduction motivated by the
probabilistic formulation of principal component analysis. Nonlinearity is
achieved by specifying different transformation matrices at different locations
of the latent space and smoothing the transformation using a Markov random
field type prior. The computation is made feasible by the recent advances in
sampling from von Mises-Fisher distributions.
