Block Diagonal Graph Embedded Discriminative Regression for Image Representation

Linear regression, a widely-used method in representation learning, initially faced limitations in incorporating structural information within the regression space. Existing models designed to extract structural insights often prioritize the proximity of data points in feature space, while overlooking crucial interdependencies and co-occurrences among them. In response to the challenges posed by the inherent limitations, we introduce a novel representation learning model based on linear regression. This model seamlessly integrates three essential modules: flexible regression learning, graph embedding learning, and embedded block-diagonal self-representation learning. The collaborative functioning of these modules establishes a closed optimization loop. The self-representation matrix directly captures the latent graph structure across the entire data domain, without the need for setting additional parameters such as the neighborhood scale of the graph. Concurrently, it facilitates flexible regression learning by uncovering latent structural patterns. Experimental results on multiple benchmark datasets demonstrate the superiority of our approach over state-of-the-art methods, providing a more comprehensive solution for representation learning.