  Semi-supervised clustering problems focus on clustering data with labels. In
this paper,we consider the semi-supervised hypergraph problems. We use the
hypergraph related tensor to construct an orthogonal constrained optimization
model. The optimization problem is solved by a retraction method, which employs
the polar decomposition to map the gradient direction in the tangent space to
the Stefiel manifold. A nonmonotone curvilinear search is implemented to
guarantee reduction in the objective function value. Convergence analysis
demonstrates that the first order optimality condition is satisfied at the
accumulation point. Experiments on synthetic hypergraph and hypergraph given by
real data demonstrate the effectivity of our method.
