  This study investigates the problem of multi-view clustering, where multiple
views contain consistent information and each view also includes complementary
information. Exploration of all information is crucial for good multi-view
clustering. However, most traditional methods blindly or crudely combine
multiple views for clustering and are unable to fully exploit the valuable
information. Therefore, we propose a method that involves consistent and
complementary graph-regularized multi-view subspace clustering (GRMSC), which
simultaneously integrates a consistent graph regularizer with a complementary
graph regularizer into the objective function. In particular, the consistent
graph regularizer learns the intrinsic affinity relationship of data points
shared by all views. The complementary graph regularizer investigates the
specific information of multiple views. It is noteworthy that the consistent
and complementary regularizers are formulated by two different graphs
constructed from the first-order proximity and second-order proximity of
multiple views, respectively. The objective function is optimized by the
augmented Lagrangian multiplier method in order to achieve multi-view
clustering. Extensive experiments on six benchmark datasets serve to validate
the effectiveness of the proposed method over other state-of-the-art multi-view
clustering methods.
