  Multi-view clustering is an important and fundamental problem. Many
multi-view subspace clustering methods have been proposed, and most of them
assume that all views share a same coefficient matrix. However, the underlying
information of multi-view data are not fully exploited under this assumption,
since the coefficient matrices of different views should have the same
clustering properties rather than be uniform among multiple views. To this end,
this paper proposes a novel Constrained Bilinear Factorization Multi-view
Subspace Clustering (CBF-MSC) method. Specifically, the bilinear factorization
with an orthonormality constraint and a low-rank constraint is imposed for all
coefficient matrices to make them have the same trace-norm instead of being
equivalent, so as to explore the consensus information of multi-view data more
fully. Finally, an Augmented Lagrangian Multiplier (ALM) based algorithm is
designed to optimize the objective function. Comprehensive experiments tested
on nine benchmark datasets validate the effectiveness and competitiveness of
the proposed approach compared with several state-of-the-arts.
