  Structured prediction provides a general framework to deal with supervised
problems where the outputs have semantically rich structure. While classical
approaches consider finite, albeit potentially huge, output spaces, in this
paper we discuss how structured prediction can be extended to a continuous
scenario. Specifically, we study a structured prediction approach to manifold
valued regression. We characterize a class of problems for which the considered
approach is statistically consistent and study how geometric optimization can
be used to compute the corresponding estimator. Promising experimental results
on both simulated and real data complete our study.
