  An important issue in cosmology is reconstructing the effective dark energy
equation of state directly from observations. With so few physically motivated
models, future dark energy studies cannot only be based on constraining a dark
energy parameter space. We present a new non-parametric method which can
accurately reconstruct a wide variety of dark energy behaviour with no prior
assumptions about it. It is simple, quick and relatively accurate, and involves
no expensive explorations of parameter space. The technique uses principal
component analysis and a combination of information criteria to identify real
features in the data, and tailors the fitting functions to pick up trends and
smooth over noise. We find that we can constrain a large variety of w(z) models
to within 10-20 % at redshifts z<1 using just SNAP-quality data.
