  As a concrete application of multi-view learning, multi-view classification
improves the traditional classification methods significantly by integrating
various views optimally. Although most of the previous efforts have been
demonstrated the superiority of multi-view learning, it can be further improved
by comprehensively embedding more powerful cross-view interactive information
and a more reliable multi-view fusion strategy in intensive studies. To fulfill
this goal, we propose a novel multi-view learning framework to make the
multi-view classification better aimed at the above-mentioned two aspects. That
is, we seamlessly embed various intra-view information, cross-view
multi-dimension bilinear interactive information, and a new view ensemble
mechanism into a unified framework to make a decision via the optimization. In
particular, we train different deep neural networks to learn various intra-view
representations, and then dynamically learn multi-dimension bilinear
interactive information from different bilinear similarities via the bilinear
function between views. After that, we adaptively fuse the representations of
multiple views by flexibly tuning the parameters of the view-weight, which not
only avoids the trivial solution of weight but also provides a new way to
select a few discriminative views that are beneficial to make a decision for
the multi-view classification. Extensive experiments on six publicly available
datasets demonstrate the effectiveness of the proposed method.
