Tensor Learning in Multi-view Kernel PCA

摘要

In many real-life applications data can be described through multiple representations, or views. Multi-view learning aims at combining the information from all views, in order to obtain a better performance. Most well-known multi-view methods optimize some form of correlation between two views, while in many applications there are three or more views available. This is usually tackled by optimizing the correlations pairwise. However, this ignores the higher-order correlations that could only be discovered when exploring all views simultaneously. This paper proposes novel multi-view Kernel PCA models. By introducing a model tensor, the proposed models aim to include the higher-order correlations between all views. The paper further explores the use of these models as multi-view dimensionality reduction techniques and shows experimental results on several real-life datasets. These experiments demonstrate the merit of the proposed methods.