  Distinguishing the importance of views has proven to be quite helpful for
semi-supervised multi-view learning models. However, existing strategies cannot
take advantage of semi-supervised information, only distinguishing the
importance of views from a data feature perspective, which is often influenced
by low-quality views then leading to poor performance. In this paper, by
establishing a link between labeled data and the importance of different views,
we propose an auto-weighted strategy to evaluate the importance of views from a
label perspective to avoid the negative impact of unimportant or low-quality
views. Based on this strategy, we propose a transductive semi-supervised
auto-weighted multi-view classification model. The initialization of the
proposed model can be effectively determined by labeled data, which is
practical. The model is decoupled into three small-scale sub-problems that can
efficiently be optimized with a local convergence guarantee. The experimental
results on classification tasks show that the proposed method achieves optimal
or sub-optimal classification accuracy at the lowest computational cost
compared to other related methods, and the weight change experiments show that
our proposed strategy can distinguish view importance more accurately than
other related strategies on multi-view datasets with low-quality views.
