Ordering as privileged information
  We propose to accelerate the rate of convergence of the pattern recognition
task by directly minimizing the variance diameters of certain hypothesis
spaces, which are critical quantities in fast-convergence results.We show that
the variance diameters can be controlled by dividing hypothesis spaces into
metric balls based on a new order metric. This order metric can be minimized as
an ordinal regression problem, leading to a LUPI (Learning Using Privileged
Information) application where we take the privileged information as some
desired ordering, and construct a faster-converging hypothesis space by
empirically restricting some larger hypothesis space according to that
ordering. We give a risk analysis of the approach. We discuss the difficulties
with model selection and give an innovative technique for selecting multiple
model parameters. Finally, we provide some data experiments.
