A Complete Discriminative Tensor Representation Learning for Two-Dimensional Correlation Analysis

TL;DR

This paper introduces a discriminative tensor learning method that enhances 2D correlation analysis by extracting more effective discriminant features, leading to improved performance on 2D data analysis tasks.

Contribution

It proposes a novel complete discriminative tensor representation learning approach based on linear correlation analysis for 2D signals, addressing the limitations of unsupervised 2DCCA.

Findings

Outperforms state-of-the-art methods on multiple datasets

Effectively extracts discriminant representations from 2D data

Improves classification performance in 2D data analysis

Abstract

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.