Abstract In this paper, we propose a speed-up approach for subclass discriminant analysis and formulate a novel efficient multi-view solution to it. The speed-up approach is developed based on graph embedding and spectral regression approaches that involve eigendecomposition of the corresponding Laplacian matrix and regression to its eigenvectors. We show that by exploiting the structure of the between-class Laplacian matrix, the eigendecomposition step can be substituted with a much faster process. Furthermore, we formulate a novel criterion for multi-view subclass discriminant analysis and show that an efficient solution to it can be obtained in a similar manner to the single-view case. We evaluate the proposed methods on nine single-view and nine multi-view datasets and compare them with related existing approaches. Experimental results show that the proposed solutions achieve competitive performance, often outperforming the existing methods. At the same time, they significantly decrease the training time.

中文翻译：

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子类判别分析的加速和多视图扩展

摘要 在本文中，我们提出了一种用于子类判别分析的加速方法，并为其制定了一种新颖的高效多视图解决方案。加速方法是基于图嵌入和谱回归方法开发的，这些方法涉及相应拉普拉斯矩阵的特征分解及其特征向量的回归。我们表明，通过利用类间拉普拉斯矩阵的结构，特征分解步骤可以用更快的过程代替。此外，我们为多视图子类判别分析制定了一个新标准，并表明可以以与单视图情况类似的方式获得它的有效解决方案。我们在九个单视图和九个多视图数据集上评估所提出的方法，并将它们与相关的现有方法进行比较。实验结果表明，所提出的解决方案实现了有竞争力的性能，通常优于现有方法。同时，它们显着减少了训练时间。