| 摘要: |
| 类属特征是一种解决多标记分类问题的有效策略. 通过为不同标记的判别过程提供不同的定制特征, 类属特征能够同时兼顾各个标记潜在不同的判别偏好, 进而改善多标记分类模型的泛化性能. 为学习类属特征, 已有方法通常关注于利用特征处理技术对样本中标记判别的相关特征进行提取. 不同于上述常规做法, 尝试从特征不变性的视角解决类属特征的学习问题: 通过操纵标记判别的无关特征, 为分类模型注入关于无关特征的不变性, 从而充分地兼顾各个标记的判别偏好. 相应地, 提出一种基于不变性注入的多标记类属特征学习方法INVA. INVA方法通过估计特征协方差矩阵捕获各个标记的类内特征变化, 从而辨识标记判别的无关特征; 通过求解扰动风险最小化问题, 赋予分类模型关于无关特征变化的不变性. 进一步地, 推导扰动风险最小化问题的上界, 提高了方法的计算效率. 在多标记基准数据集上, 与已有方法进行全面的实验对比, 验证所提方法的有效性. |
| 关键词: 多标记分类 类属特征 特征不变性 |
| DOI：10.13328/j.cnki.jos.007447 |
| 分类号:TP18 |
| 基金项目:国家自然科学基金(62225602) |
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| Multi-label Label-specific Feature Learning Based on Invariance Injection |
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HANG Jun-Yi1,2, ZHANG Min-Ling1,2
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1.School of Computer Science and Engineering, Southeast University, Nanjing 210096, China;2.Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 210096, China
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| Abstract: |
| Label-specific features serve as an effective strategy for addressing multi-label classification tasks. By tailoring discriminative features to the individual preferences of each label, such features enhance the generalization capability of classification models. Existing methods typically focus on manipulating features to extract those relevant to label discrimination. Rather than following this conventional approach, this study explores a novel perspective based on feature invariance for label-specific feature learning. Specifically, invariance is injected into classifiers with respect to label-irrelevant features by intentionally manipulating these features for each class label. Accordingly, an invariance-based label-specific feature learning method, termed INVA, is proposed. INVA estimates the feature covariance matrix for each label to capture intra-class variation, thus identifying label-irrelevant features. Classifiers are then endowed with invariance to these features by solving a perturbation risk minimization problem. Furthermore, an upper bound of the perturbation risk is derived to enhance computational efficiency. Comprehensive experiments on standard multi-label benchmark datasets demonstrate the effectiveness of the proposed method. |
| Key words: multi-label classification label-specific feature feature invariance |
