  A standard approach for model reduction of linear input-output systems is
balanced truncation, which is based on the controllability and observability
properties of the underlying system. The related dominant subspace projection
model reduction method similarly utilizes these system properties, yet instead
of balancing, the associated subspaces are directly conjoined. In this work we
extend the dominant subspace approach by computation via the cross Gramian for
linear systems, and describe an a-priori error indicator for this method.
Furthermore, efficient computation is discussed alongside numerical examples
illustrating these findings.
