# Recovering Accurate Labeling Information from Partially Valid Data for Effective Multi-Label Learning

arXiv: https://arxiv.org/abs/2006.11488 · HTML: https://mlsanity.com/paper/2006.11488/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [Deep Partial Multi-Label Learning with Graph Disambiguation](https://mlsanity.com/paper/2305.05882/) (2023)
- [Reconsidering Feature Structure Information and Latent Space Alignment in Partial Multi-label Feature Selection](https://mlsanity.com/paper/2503.10115/) (2025)
- [Noise-Resistant Label Reconstruction Feature Selection for Partial Multi-Label Learning](https://mlsanity.com/paper/2506.04669/) (2025)

## Abstract (arXiv)

Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid. To address the noisy issue, the existing PML methods basically recover the ground-truth labels by leveraging the ground-truth confidence of the candidate label, ie the likelihood of a candidate label being a ground-truth one. However, they neglect the information from non-candidate labels, which potentially contributes to the ground-truth label recovery. In this paper, we propose to recover the ground-truth labels, ie estimating the ground-truth confidences, from the label enrichment, composed of the relevance degrees of candidate labels and irrelevance degrees of non-candidate labels. Upon this observation, we further develop a novel two-stage PML method, namely emphunderlinePartial underlineMulti-underlineLabel underlineLearning with underlineLabel underlineEnrichment-underlineRecovery (baby), where in the first stage, it estimates the label enrichment with unconstrained label propagation, then jointly learns the ground-truth confidence and multi-label predictor given the label enrichment. Experimental results validate that baby outperforms the state-of-the-art PML methods.