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Learning Common and Label-Specific Features for Multi-Label Classification With Missing Labels
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DOI：10.1109/ACCESS.2024.3411095.png)
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Abstract
En 中文
Multi-label learning is a subfield of machine learning that addresses the issue of each instance belonging to numerous class labels at the same time. However, in some real applications, we can only receive a partial set of labels for each instance due to the difficulty and high cost of labeling data. The vast majority of existing multi-label classification methods on missing labels rely on first- or second-order label correlation learning to fill in the original label space while building multi-label learning models with label-specific features; nevertheless, the single label correlation learning mechanism used in these methods is insufficient to maintain the consistency of the feature-label space. To address this issue, we propose the CLSML approach, which incorporates higher-order label correlation learning constraints in the classifier training model to complete missing labels while training the classifier. In addition, to improve the consistency of the feature-label space, we develop a two-stage second-order label correlation learning technique based on cosine similarity to further confine the label output. Furthermore, we employ the l(2,1) -norm regularizer to learn label-specific feature representations, followed by the l(2,1) -norm regularizer to constrain the row sparsity of the classification matrix and select label-common features. Experimental results comparing ten cutting-edge multi-label learning algorithms with missing labels on fourteen multi-label benchmark datasets demonstrate the effectiveness of our suggested approach.
Keywords:
Correlation
Feature extraction
Training
Representation learning
Classification algorithms
Training data
Sparse matrices
Labeling
Multi-label learning
label correlations
missing labels
label-specific feature
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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