Partial multi-label learning: exploration of binary ground-truth labels

Yan Hu, Xiaozhao Fang, Weijun Lv, Peipei Kang

Abstract: Partial multi-label learning (PML) aims to accurately predict multi-labels for unknown instances with noise labels in training set. Accurate identification of ground-truth labels is essential to optimize performance. The accuracy of ground-truth labels affects label correlation capture, which guides classifier learning. However, current methods often rely on approximation of ground-truth labels through intermediate variables, rather than restoring binary ground-truth labels directly. To solve this problem, we propose a partial multi-label learning algorithm by exploring binary ground-truth labels (PML-EBGL). First, the candidate label matrix is decomposed into a ground-truth label matrix and a noise matrix. Then, the noise matrix is constrained using the l 1 norm for sparsity, and the binary ground-truth label matrix is used to explore label correlation, which is transferred to classifier by using the Laplacian term. Finally, the rotation matrix is added after the classifier, and the instance is projected onto the binary label matrix. Extensive experiments show that PML-EBGL outperforms state-of-the-art methods.