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.
翻译:多视角学习经常用于数据科学。双向相关性最大化是探索多种观点共识的经典方法。由于双向相关性是两种观点所固有的,因此对齐相关性可以使更多观点的扩展多样化,而且各种观点之间的内在相互联系通常会丢失。为了解决这一问题,我们提议最大限度地扩大更高顺序的关联性。这可以被表述为与多视图数据较高顺序相关度的低排序近似问题。我们使用生成的多级方法解决低排序近似问题。实际多视图数据的数字结果表明,这种方法一贯优于以往的方法。