论文标题
多视图学习的高阶相关分析
Higher Order Correlation Analysis for Multi-View Learning
论文作者
论文摘要
多视图学习经常用于数据科学。成对相关性最大化是探索多种视图共识的经典方法。由于成对相关性是两种视图固有的，因此更多视图的扩展可能是多元化的，并且观点之间的固有互连通常会丢失。为了解决这个问题，我们建议最大化高阶相关性。这可以作为低等级近似问题进行表述，并具有多视图数据的高阶相关张量。我们使用生成多项式方法来解决低级近似问题。实际多视图数据的数值结果表明，此方法始终优于先前的现有方法。
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.
