  We propose to solve a label ranking problem as a structured output regression
task. We adopt a least square surrogate loss approach that solves a supervised
learning problem in two steps: the regression step in a well-chosen feature
space and the pre-image step. We use specific feature maps/embeddings for
ranking data, which convert any ranking/permutation into a vector
representation. These embeddings are all well-tailored for our approach, either
by resulting in consistent estimators, or by solving trivially the pre-image
problem which is often the bottleneck in structured prediction. We also propose
their natural extension to the case of partial rankings and prove their
efficiency on real-world datasets.
