A bi-level metric learning framework via self-paced learning weighting

•We propose a novel bi-level framework for learning an effective Mahalanobis distance metric.•We introduce an implementation of the proposed framework based on the self-paced learning method and design the corresponding optimization algorithm.•The robustness of the proposed model is improved by reducing the effect of noisy samples on the model via a self-paced learning regular term.•Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art methods on several benchmark datasets.