[1]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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Transformable operator-valued kernels driven by multi-label datasets

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 2 Page number: 365-374 Column: 学术论文—机器学习 Public date: 2026-04-30
Title:
Transformable operator-valued kernels driven by multi-label datasets
Author(s):
WANG Zhenxin1;  CHEN Degang2;  CHE Xiaoya2
1. North China Electric Power University, School of Control and Computer Engineering, Beijing 102200, China;
2. North China Electric Power University, Department of Mathematics and Physics, Beijing 102200, China
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
CLC:
TP181
DOI:
10.11992/tis.202503021
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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