Normalized Cut is a widely used technique for solving a
variety of problems. Although finding the optimal normalized
cut has proven to be NP-hard, spectral relaxations can
be applied and the problem of minimizing the normalized
cut can be approximately solved using eigen-computations.
However, it is a challenge to incorporate prior information
in this approach. In this paper, we express prior knowledge
by linear constraints on the solution, with the goal of minimizing
the normalized cut criterion with respect to these
constraints. We develop a fast and effective algorithm that
is guaranteed to converge. Convincing results are achieved
on image segmentation tasks, where the prior knowledge is
given as the grouping information of features.