As an effective tool for two-dimensional data analysis, two-dimensional canonical correlation analysis (2DCCA) is not only capable of preserving the intrinsic structural information of original two-dimensional (2D) data, but also reduces the computational complexity effectively. However, due to the unsupervised nature, 2DCCA is incapable of extracting sufficient discriminatory representations, resulting in an unsatisfying performance. In this letter, we propose a complete discriminative tensor representation learning (CDTRL) method based on linear correlation analysis for analyzing 2D signals (e.g. images). This letter shows that the introduction of the complete discriminatory tensor representation strategy provides an effective vehicle for revealing, and extracting the discriminant representations across the 2D data sets, leading to improved results. Experimental results show that the proposed CDTRL outperforms state-of-the-art methods on the evaluated data sets.

中文翻译：

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二维相关分析的完整判别张量表示学习

作为二维数据分析的有效工具，二维典型相关分析（2DCCA）不仅能够保留原始二维（2D）数据的内在结构信息，还可以有效降低计算复杂度。然而，由于无监督的性质，2DCCA 无法提取足够的歧视性表示，导致性能不令人满意。在这封信中，我们提出了一种基于线性相关分析的完整判别张量表示学习 (CDTRL) 方法，用于分析 2D 信号（例如图像）。这封信表明，引入完整的判别张量表示策略为揭示和提取二维数据集的判别表示提供了有效的工具，导致结果的改善。实验结果表明，所提出的 CDTRL 在评估的数据集上优于最先进的方法。