A Complete Discriminative Tensor Representation Learning for Two-Dimensional Correlation Analysis
This work addresses a domain-specific problem in 2D signal analysis, offering an incremental improvement over existing methods.
The paper tackled the problem of insufficient discriminatory representations in two-dimensional canonical correlation analysis (2DCCA) for 2D data like images, proposing a complete discriminative tensor representation learning (CDTRL) method that outperformed state-of-the-art methods on evaluated datasets.
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.