Discriminative graph regularized representation learning for recognition.

ABSTRACT: Feature extraction has been extensively studied in the machine learning field as it plays a critical role in the success of various practical applications. To uncover compact low-dimensional feature representations with strong generalization and discrimination capabilities for recognition tasks, in this paper, we present a novel discriminative graph regularized representation learning (DGRL) model that is able to elegantly incorporate both global and local geometric structures as well as the label structure of data into a joint framework. Specifically, DGRL first integrates dimension reduction into ridge regression rather than treated them as two irrelevant steps, which enables us to capture the underlying subspace structure and correlation patterns among classes. Additionally, a graph reg

SUBMITTER: Qi J 

PROVIDER: S-EPMC12270171 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature