  The plenty information from multiple views data as well as the complementary
information among different views are usually beneficial to various tasks,
e.g., clustering, classification, de-noising. Multi-view subspace clustering is
based on the fact that the multi-view data are generated from a latent
subspace. To recover the underlying subspace structure, the success of the
sparse and/or low-rank subspace clustering has been witnessed recently. Despite
some state-of-the-art subspace clustering approaches can numerically handle
multi-view data, by simultaneously exploring all possible pairwise correlation
within views, the high order statistics is often disregarded which can only be
captured by simultaneously utilizing all views. As a consequence, the
clustering performance for multi-view data is compromised. To address this
issue, in this paper, a novel multi-view clustering method is proposed by using
\textit{t-product} in third-order tensor space. Based on the circular
convolution operation, multi-view data can be effectively represented by a
\textit{t-linear} combination with sparse and low-rank penalty using
"self-expressiveness". Our extensive experimental results on facial, object,
digits image and text data demonstrate that the proposed method outperforms the
state-of-the-art methods in terms of many criteria.
