Uncovering hidden patterns: low-rank label correlations for multi-label weak-label learning

Abstract

Multi-label learning has emerged as a prominent research area in machine learning, as each instance can be associated with multiple class labels. However, many multi-label learning algorithms assume that the label space is complete, whereas in real-world applications, we often only have access to partial label information. To address this issue, we propose a novel Multi-label Weak-label learning algorithm via Low-rank Label correlations (MW2L). First, we propagate the structural and semantic information from the feature space to the label space to effectively capture label-related information and recover lost labels. Second, we incorporate global and local low-rank label correlation information to ensure that the label-related matrix is informative. Last, we use label correlations to supplement the original weak-label matrix and form a unified learning framework. We evaluate the performance of our approach on several benchmark datasets and show that it outperforms state-of-the-art methods in terms of accuracy and robustness to weak-label noise. The proposed approach can effectively handle incomplete and noisy weak labels in multi-label learning and outperforms existing methods.