[1]汪振鑫,陈德刚,车晓雅.多标记数据驱动的可变换算子值核[J].智能系统学报,2026,21(2):365-374.[doi:10.11992/tis.202503021]
WANG Zhenxin,CHEN Degang,CHE Xiaoya.Transformable operator-valued kernels driven by multi-label datasets[J].CAAI Transactions on Intelligent Systems,2026,21(2):365-374.[doi:10.11992/tis.202503021]
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WANG Zhenxin,CHEN Degang,CHE Xiaoya.Transformable operator-valued kernels driven by multi-label datasets[J].CAAI Transactions on Intelligent Systems,2026,21(2):365-374.[doi:10.11992/tis.202503021]
多标记数据驱动的可变换算子值核
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
21
期数:
2026年第2期
页码:
365-374
栏目:
学术论文—机器学习
出版日期:
2026-03-05
- Title:
- Transformable operator-valued kernels driven by multi-label datasets
- Keywords:
- multi-label learning; operator-valued kernel; transformable operator-valued kernel; partial trace kernel; label correlation; instance-level feature importance distribution; interaction information; kernel alignment
- 分类号:
- TP181
- 摘要:
- 算子值核是取值为希尔伯特空间上算子的二元函数，在机器学习领域中旨在更好地描述多任务学习中不同任务之间的关联性。多标记学习是一种特殊的多任务学习，本文基于核对齐方法从多标记数据集中学习算子值核并构建多标记学习的预测模型。1)利用核对齐方法学习样例级特征重要度分布；2)基于样例级特征重要度分布构造算子值核，证明其不仅是偏迹核而且是可变换算子值核，且其对应核矩阵中的每个分块刻画了样例间标记相关性的交互信息；3)设计基于可变换算子值核的多标记学习算法，在9个多标记数据集上与4种高性能算法进行对比实验，结果验证了所提算法的有效性。
- Abstract:
- An operator-valued kernel is a binary function that takes the value of an operator on Hilbert space, which in the field of machine learning aims to better describe the correlation between different tasks in multi-task learning. Multi-label learning is a special kind of multi-task learning, in this paper, we learn operator-valued kernels from multi-label datasets based on the kernel alignment method and construct a prediction model for multi-label learning. Firstly, we use kernel alignment method to learn the instance-level feature importance distribution; secondly, we construct operator-valued kernel based on the instance-level feature importance distribution, and prove that it is not only partial trace kernel but also transformable operator-valued kernel, and that each block of its corresponding kernel matrix depicts the interaction information of label correlation among the samples; lastly, we design the multi-label learning algorithms based on transformable operator-valued kernel, and conduct comparative experiments with four high-performance algorithms on nine multi-label datasets, the results verify effectiveness of our proposed algorithm.
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备注/Memo
收稿日期:2025-3-23。
基金项目:国家自然科学基金项目（12571496,12201213）.
作者简介:汪振鑫，博士研究生，主要研究方向为机器学习。发表学术论文2篇。E-mail：465095864@qq.com。;陈德刚，教授，博士生导师，主要研究方向为机器学习和数据挖掘。发表学术论文150余篇。E-mail：chengdegang@263.net。;车晓雅，讲师，主要研究方向为机器学习与数据挖掘。发表学术论文10篇。E-mail：chexiaoya@163.com。
通讯作者:陈德刚. E-mail：chengdegang@263.net
更新日期/Last Update:
1900-01-01