  To investigate objects without a describable notion of distance, one can
gather ordinal information by asking triplet comparisons of the form "Is object
$x$ closer to $y$ or is $x$ closer to $z$?" In order to learn from such data,
the objects are typically embedded in a Euclidean space while satisfying as
many triplet comparisons as possible. In this paper, we introduce empirical
uncertainty estimates for standard embedding algorithms when few noisy triplets
are available, using a bootstrap and a Bayesian approach. In particular,
simulations show that these estimates are well calibrated and can serve to
select embedding parameters or to quantify uncertainty in scientific
applications.
