  With powerful ability to exploit latent structure of self-representation
information, different tensor decompositions have been employed into low rank
multi-view clustering (LRMVC) models for achieving significant performance.
However, current approaches suffer from a series of problems related to those
tensor decomposition, such as the unbalanced matricization scheme, rotation
sensitivity, deficient correlations capture and so forth. All these will lead
to LRMVC having insufficient access to global information, which is contrary to
the target of multi-view clustering. To alleviate these problems, we propose a
new tensor decomposition called Tucker-O-Minus Decomposition (TOMD) for
multi-view clustering. Specifically, based on the Tucker format, we
additionally employ the O-minus structure, which consists of a circle with an
efficient bridge linking two weekly correlated factors. In this way, the core
tensor in Tucker format is replaced by the O-minus architecture with a more
balanced structure, and the enhanced capacity of capturing the global low rank
information will be achieved. The proposed TOMD also provides more compact and
powerful representation abilities for the self-representation tensor,
simultaneously. The alternating direction method of multipliers is used to
solve the proposed model TOMD-MVC. Numerical experiments on six benchmark data
sets demonstrate the superiority of our proposed method in terms of F-score,
precision, recall, normalized mutual information, adjusted rand index, and
accuracy.
