智能系统学报2026，Vol.21Issue(2)：365-374,10.DOI:10.11992/tis.202503021
多标记数据驱动的可变换算子值核
Transformable operator-valued kernels driven by multi-label datasets
摘要
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-val-ued 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 interac-tion 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.关键词
多标记学习/算子值核/可变换算子值核/偏迹核/标记相关性/样例级特征重要度分布/交互信息/核对齐Key words
multi-label learning/operator-valued kernel/transformable operator-valued kernel/partial trace kernel/la-bel correlation/instance-level feature importance distribution/interaction information/kernel alignment分类
信息技术与安全科学引用本文复制引用
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):365-374,10.基金项目
国家自然科学基金项目(12571496,12201213). （12571496,12201213）
