  Multi-view learning is frequently used in data science. The pairwise
correlation maximization is a classical approach for exploring the consensus of
multiple views. Since the pairwise correlation is inherent for two views, the
extensions to more views can be diversified and the intrinsic interconnections
among views are generally lost. To address this issue, we propose to maximize
higher order correlations. This can be formulated as a low rank approximation
problem with the higher order correlation tensor of multi-view data. We use the
generating polynomial method to solve the low rank approximation problem.
Numerical results on real multi-view data demonstrate that this method
consistently outperforms prior existing methods.
