# Multi-Label Learning with Label Enhancement

arXiv: https://arxiv.org/abs/1706.08323 · HTML: https://mlsanity.com/paper/1706.08323/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2017.

## Built on by (most cited first, 4 of 4)

- [Label Distribution Learning from Logical Label](https://mlsanity.com/paper/2303.06847/) (2023)
- [Improving Multi-Label Contrastive Learning by Leveraging Label Distribution](https://mlsanity.com/paper/2501.19145/) (2025)
- [Data Augmentation For Label Enhancement](https://mlsanity.com/paper/2303.11698/) (2023)
- [Scalable Label Distribution Learning for Multi-Label Classification](https://mlsanity.com/paper/2311.16556/) (2023)

## Abstract (arXiv)

The task of multi-label learning is to predict a set of relevant labels for the unseen instance. Traditional multi-label learning algorithms treat each class label as a logical indicator of whether the corresponding label is relevant or irrelevant to the instance, i.e., +1 represents relevant to the instance and -1 represents irrelevant to the instance. Such label represented by -1 or +1 is called logical label. Logical label cannot reflect different label importance. However, for real-world multi-label learning problems, the importance of each possible label is generally different. For the real applications, it is difficult to obtain the label importance information directly. Thus we need a method to reconstruct the essential label importance from the logical multilabel data. To solve this problem, we assume that each multi-label instance is described by a vector of latent real-valued labels, which can reflect the importance of the corresponding labels. Such label is called numerical label. The process of reconstructing the numerical labels from the logical multi-label data via utilizing the logical label information and the topological structure in the feature space is called Label Enhancement. In this paper, we propose a novel multi-label learning framework called LEMLL, i.e., Label Enhanced Multi-Label Learning, which incorporates regression of the numerical labels and label enhancement into a unified framework. Extensive comparative studies validate that the performance of multi-label learning can be improved significantly with label enhancement and LEMLL can effectively reconstruct latent label importance information from logical multi-label data.