  Multi-view clustering is a learning paradigm based on multi-view data. Since
statistic properties of different views are diverse, even incompatible, few
approaches implement multi-view clustering based on the concatenated features
straightforward. However, feature concatenation is a natural way to combine
multi-view data. To this end, this paper proposes a novel multi-view subspace
clustering approach dubbed Feature Concatenation Multi-view Subspace Clustering
(FCMSC), which boosts the clustering performance by exploring the consensus
information of multi-view data. Specifically, multi-view data are concatenated
into a joint representation firstly, then, $l_{2,1}$-norm is integrated into
the objective function to deal with the sample-specific and cluster-specific
corruptions of multiple views. Moreover, a graph regularized FCMSC is also
proposed in this paper to explore both the consensus information and
complementary information of multi-view data for clustering. It is noteworthy
that the obtained coefficient matrix is not derived by simply applying the
Low-Rank Representation (LRR) to concatenated features directly. Finally, an
effective algorithm based on the Augmented Lagrangian Multiplier (ALM) is
designed to optimize the objective functions. Comprehensive experiments on six
real-world datasets illustrate the superiority of the proposed methods over
several state-of-the-art approaches for multi-view clustering.
