  In many real-world applications, data are often unlabeled and comprised of
different representations/views which often provide information complementary
to each other. Although several multi-view clustering methods have been
proposed, most of them routinely assume one weight for one view of features,
and thus inter-view correlations are only considered at the view-level. These
approaches, however, fail to explore the explicit correlations between features
across multiple views. In this paper, we introduce a tensor-based approach to
incorporate the higher-order interactions among multiple views as a tensor
structure. Specifically, we propose a multi-linear multi-view clustering (MMC)
method that can efficiently explore the full-order structural information among
all views and reveal the underlying subspace structure embedded within the
tensor. Extensive experiments on real-world datasets demonstrate that our
proposed MMC algorithm clearly outperforms other related state-of-the-art
methods.
