Recently, multi-view feature learning technique has attracted lots of research interest. Discriminant analysis-based multi-view feature learning is an important research branch. Although some multi-view discriminant analysis methods have been presented, there still exists room for improvement. How to effectively explore the discriminant and local geometrical structure information simultaneously from multiple views is still an important research topic. In this paper, we propose a novel approach named uncorrelated locality-sensitive multi-view discriminant analysis, which jointly learns multiple view-specific transformations, such that in the projected subspace for each view, the within-class nearby samples are close to each other, while the between-class nearby samples are far apart. We provide a multi-view sample distance term to promote the one-to-one data consistency across views. Furthermore, we design uncorrelated constraints to reduce the redundancy among the transformations. Experiments on two widely used datasets demonstrate the effectiveness of the proposed approach.

中文翻译：

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不相关的局部敏感多视图判别分析

近年来，多视图特征学习技术引起了很多研究兴趣。基于判别分析的多视图特征学习是一个重要的研究分支。尽管已经提出了一些多视角判别分析方法，但是仍然存在改进的空间。如何有效地从多个角度同时探索判别和局部几何结构信息仍然是一个重要的研究课题。在本文中，我们提出了一种名为不相关的局部敏感多视图判别分析的新方法，该方法可共同学习多个视图特定的变换，从而在每个视图的投影子空间中，类内附近的样本彼此接近，而类间附近的样本相距较远。我们提供了多视图样本距离项，以促进跨视图的一对一数据一致性。此外，我们设计了不相关的约束以减少转换之间的冗余。在两个广泛使用的数据集上进行的实验证明了该方法的有效性。