  Matched-filtering for the identification of compact object mergers in
gravitational-wave antenna data involves the comparison of the data stream to a
bank of template gravitational waveforms. Typically the template bank is
constructed from phenomenological waveform models since these can be evaluated
for an arbitrary choice of physical parameters. Recently it has been proposed
that singular value decomposition (SVD) can be used to reduce the number of
templates required for detection. As we show here, another benefit of SVD is
its removal of biases from the phenomenological templates along with a
corresponding improvement in their ability to represent waveform signals
obtained from numerical relativity (NR) simulations. Using these ideas, we
present a method that calibrates a reduced SVD basis of phenomenological
waveforms against NR waveforms in order to construct a new waveform approximant
with improved accuracy and faithfulness compared to the original
phenomenological model. The new waveform family is given numerically through
the interpolation of the projection coefficients of NR waveforms expanded onto
the reduced basis and provides a generalized scheme for enhancing
phenomenological models.
