# Data Augmentation For Label Enhancement

arXiv: https://arxiv.org/abs/2303.11698 · HTML: https://mlsanity.com/paper/2303.11698/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 39 references that are in the map.
- First posted on arXiv 21 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [Label Distribution Learning](https://mlsanity.com/paper/1408.6027/) (2014)
- [Towards Speaker Age Estimation with Label Distribution Learning](https://mlsanity.com/paper/2202.11424/) (2022)
- [Generalized Label Enhancement with Sample Correlations](https://mlsanity.com/paper/2004.03104/) (2020)
- [Multi-Label Learning with Label Enhancement](https://mlsanity.com/paper/1706.08323/) (2017)

## Abstract (arXiv)

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: (i) They use logical label to train mappings to LD, but the supervision information is too loose, which can lead to inaccurate model prediction; (ii) They ignore feature redundancy and use the collected features directly. To solve (i), we use the topology of the feature space to generate more accurate label-confidence. To solve (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.