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Progressive label enhancement
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AI
DOI：10.1016/j.patcog.2024.111172.png)
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Abstract
En 中文
Label Distribution Learning (LDL) leverages label distribution (LD) to represent instances, which helps solve label ambiguity. However, obtaining LD can be extremely challenging in many real-world scenarios. Label Enhancement (LE) has emerged as a solution to enhance logical labels to LD since logical labels are highly available. In this paper, we explore the application of dimension reduction techniques to enhance LE. We present a learning framework known as Progressive Label Enhancement (PLE). PLE progressively conducts dependency-maximization-oriented dimension reduction and LE. First, PLE generates LD by leveraging the manifold structure within the feature space induced by dependency-maximization-driven dimension reduction. Second, PLE optimizes the projection matrix for dependency maximization based on the obtained LD. Finally, extensive experiments conducted on 15 real-world datasets consistently demonstrate that PLE outperforms the other six comparative approaches.
Keywords:
Label enhancement
Label distribution learning
Multi-label learning
Label ambiguity
Dimension reduction
Manifold
Journal
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