Multi-view dimensionality reduction is an importan subject in multi-view learning. Canonical correlation analysis and its various improved forms can effectively solve this problem. But most of these algorithms do not fully consider the discriminant information and view consistency information contained in the data itself simultaneously. To solve this problem, a new multi-view dimensionality reduction algorithm, consistent discriminant correlation analysis, is proposed in this paper. The algorithm integrates the class information and the consistency information between views into the dimension reduction process. By maximizing the within-class correlations and the consistency between views, and minimizing the between-class correlations simultaneously, it extracts the low-dimensional features that are more efficient to classification. Furthermore, a kernel consistent discriminant correlation analysis is proposed. The experimental results on several data sets demonstrate the effectiveness of the proposed methods.

中文翻译：

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一致性判别相关分析

多视图降维是多视图学习中的重要课题。典型的相关分析及其各种改进形式可以有效地解决这一问题。但是，这些算法大多数都没有充分考虑判别信息，也无法同时查看数据本身中包含的一致性信息。为了解决这个问题，本文提出了一种新的多视角降维算法，即一致性判别相关分析。该算法将类别信息和视图之间的一致性信息集成到降维过程中。通过最大化类内相关性和视图之间的一致性，同时最小化类间相关性，它提取了对分类更有效的低维特征。此外，提出了核一致性判别相关分析。在几个数据集上的实验结果证明了所提出方法的有效性。