  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.
