Joint Outlier Detection and Representation Learning for Robust Multiclass Classification

Representation-based classification methods have been extensively studied in visual recognition tasks. However, some early methods were typically based on the mean squared error (MSE), which is highly sensitive to gross corruption. Although various robust strategies have been proposed, they still suffer from insufficient robustness and are sensitive to outliers. Consequently, achieving robust modeling in complex scenarios with gross corruption and outliers remains a significant challenge. To address this concern, this paper proposes a novel representation-based classification method, called Joint Outlier Detection and Representation Learning (JODRL), which integrates outlier detection and representation learning into a unified framework. By mutually boosting each other, JODRL can effectively reduce the impact of outliers and further improve robustness. Furthermore, we propose an efficient alternating optimization algorithm based on the alternating direction method of multipliers and half-quadratic theory. Experimental results on five representative benchmark datasets demonstrate that JODRL achieves significantly better performance than existing methods under various complex noise and outlier interference scenarios, fully validating the model’s effectiveness and robustness.