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Multi-label learning based on operator-valued kernels
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DOI：10.1007/s13042-025-02732-8.png)
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Abstract
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Multi-label learning aims to exploit label correlation for prediction. In this paper, we learn operator-valued kernels from multi-label dataset to achieve finer-grained characterization of label correlation. Firstly, the global importance distribution of feature set to label is calculated based on Hilbert–Schmidt Independence Criterion (HSIC). The construction of separable operator-valued kernel relies on these importance distributions to describe the global label correlation. Secondly, the instance-level feature importance distribution is further learned to develop transformable operator-valued kernel by using HSIC. The block operator kernel matrix of transformable kernel describes the instance-level label correlation. Thirdly, multi-label learning algorithms associated with operator-valued kernel are designed to tackle multi-label learning prediction tasks. In order to demonstrate the effectiveness of our proposed algorithms, the classification experiments and statistical analysis results on eight multi-label datasets are presented, and the results are compared with five high-performance algorithms.
Keywords:
Multi-label learning
Hilbert–Schmidt independence criterion
Operator-valued kernel
Label correlation
Operator kernel matrix
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