Xiangyu Fang

Correlation-Induced Label Prior for Semi-Supervised Multi-Label Learning

Semi-supervised multi-label learning (SSMLL) aims to address the challenge of limited labeled data availability in multi-label learning (MLL) by leveraging unlabeled data to improve the model's performance. Due to the difficulty of estimating the reliable label correlation on minimal multi-labeled d…