Data Augmentation For Label Enhancement

• URL: http://arxiv.org/abs/2303.11698v1
• Date: Tue, 21 Mar 2023 09:36:58 GMT
• Title: Data Augmentation For Label Enhancement
• Authors: Zhiqiang Kou, Yuheng Jia, Jing Wang, Boyu Shi, Xin Geng
• Abstract summary: Label enhancement (LE) has emerged to recover Label Distribution (LD) from logical label. We propose a novel supervised LE dimensionality reduction approach, which projects the original data into a lower dimensional feature space. The results show that our method consistently outperforms the other five comparing approaches.
• Score: 45.3351754830424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Label distribution (LD) uses the description degree to describe instances, which provides more fine-grained supervision information when learning with label ambiguity. Nevertheless, LD is unavailable in many real-world applications. To obtain LD, label enhancement (LE) has emerged to recover LD from logical label. Existing LE approach have the following problems: (\textbf{i}) They use logical label to train mappings to LD, but the supervision information is too loose, which can lead to inaccurate model prediction; (\textbf{ii}) They ignore feature redundancy and use the collected features directly. To solve (\textbf{i}), we use the topology of the feature space to generate more accurate label-confidence. To solve (\textbf{ii}), we proposed a novel supervised LE dimensionality reduction approach, which projects the original data into a lower dimensional feature space. Combining the above two, we obtain the augmented data for LE. Further, we proposed a novel nonlinear LE model based on the label-confidence and reduced features. Extensive experiments on 12 real-world datasets are conducted and the results show that our method consistently outperforms the other five comparing approaches.

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