福州大学学报（自然科学版）2026，Vol.54Issue(2)：145-153,9.DOI:10.7631/issn.1000-2243.25101
基于信息选取的标记增强算法
Label enhancement via information selection
摘要
Abstract
To address the issue of label-irrelevant information impairing the accurate recovery of label distributions,this study proposes a label enhancement approach called label enhancement via informa-tion selection(LEIS).The approach works through the collaboration of an information selection network and a recovery network:the former integrates a feature reconstruction loss and a graph regular-ization loss to filter out irrelevant information and extract discriminative label-relevant features;the latter accurately reconstructs the label distribution based on these features by minimizing a distribution reconstruction loss.The entire model is trained end-to-end,with the label-relevant features acting as a coupling bridge,enabling joint optimization via gradient back-propagation to co-optimize the informa-tion selection and distribution recovery processes.Experimental results on multiple benchmark datasets confirm that LEIS demonstrates superior and more stable recovery performance compared to various state-of-the-art methods,verifying its effectiveness and competitiveness.关键词
标记增强/样本相关性/标记分布/标记无关信息Key words
label enhancement/sample correlations/label distribution/label irrelevant information分类
信息技术与安全科学引用本文复制引用
陈诗昀,周丽萍,宋佩环,郑清海,于元隆..基于信息选取的标记增强算法[J].福州大学学报（自然科学版）,2026,54(2):145-153,9.基金项目
国家自然科学青年基金资助项目(62306074) （62306074）
福建省自然科学基金资助项目(2023J05025) （2023J05025）
