# Bringing Clustering to MLL: Weakly-Supervised Clustering for Partial Multi-Label Learning

arXiv: https://arxiv.org/abs/2604.09359 · HTML: https://mlsanity.com/paper/2604.09359/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 35 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 2 of 2)

- [The Emerging Trends of Multi-Label Learning](https://mlsanity.com/paper/2011.11197/) (2020)
- [Multi-label Classification with High-rank and High-order Label Correlations](https://mlsanity.com/paper/2207.04197/) (2022)

## Abstract (arXiv)

Label noise in multi-label learning (MLL) poses significant challenges for model training, particularly in partial multi-label learning (PML) where candidate labels contain both relevant and irrelevant labels. While clustering offers a natural approach to exploit data structure for noise identification, traditional clustering methods cannot be directly applied to multi-label scenarios due to a fundamental incompatibility: clustering produces membership values that sum to one per instance, whereas multi-label assignments require binary values that can sum to any number. We propose a novel weakly-supervised clustering approach for PML (WSC-PML) that bridges clustering and multi-label learning through membership matrix decomposition. Our key innovation decomposes the clustering membership matrix A into two components: A = Pi odot F, where Pi maintains clustering constraints while F preserves multi-label characteristics. This decomposition enables seamless integration of unsupervised clustering with multi-label supervision for effective label noise handling. WSC-PML employs a three-stage process: initial prototype learning from noisy labels, adaptive confidence-based weak supervision construction, and joint optimization via iterative clustering refinement. Extensive experiments on 24 datasets demonstrate that our approach outperforms six state-of-the-art methods across all evaluation metrics.