# Multi-label Classification with High-rank and High-order Label Correlations

arXiv: https://arxiv.org/abs/2207.04197 · HTML: https://mlsanity.com/paper/2207.04197/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 50 references that are in the map.
- First posted on arXiv 9 Jul 2022.

## Builds on (most cited first, 5 of 5)

- [Large-scale Multi-label Learning with Missing Labels](https://mlsanity.com/paper/1307.5101/) (2013)
- [Multi-Label Learning with Global and Local Label Correlation](https://mlsanity.com/paper/1704.01415/) (2017)
- [Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation](https://mlsanity.com/paper/2004.14705/) (2020)
- [Collaboration based Multi-Label Learning](https://mlsanity.com/paper/1902.03047/) (2019)
- [Cost-Sensitive Label Embedding for Multi-Label Classification](https://mlsanity.com/paper/1603.09048/) (2016)

## Built on by (most cited first, 8 of 8)

- [CLIP in Medical Imaging: A Survey](https://mlsanity.com/paper/2312.07353/) (2023)
- [Deep Learning for Multi-Label Learning: A Comprehensive Survey](https://mlsanity.com/paper/2401.16549/) (2024)
- [See Further for Parameter Efficient Fine-tuning by Standing on the Shoulders of Decomposition](https://mlsanity.com/paper/2407.05417/) (2024)
- [Multi-Label Node Classification with Label Influence Propagation](https://mlsanity.com/paper/2607.00671/) (2026)
- [Revisiting Sparsity Constraint Under High-Rank Property in Partial Multi-Label Learning](https://mlsanity.com/paper/2505.20938/) (2025)
- [Feature-Label Modal Alignment for Robust Partial Multi-Label Learning](https://mlsanity.com/paper/2604.09064/) (2026)
- [Bringing Clustering to MLL: Weakly-Supervised Clustering for Partial Multi-Label Learning](https://mlsanity.com/paper/2604.09359/) (2026)
- [ProML: Progressive Multi-label Learning](https://mlsanity.com/paper/2610.15614/) (2026)

## Abstract (arXiv)

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropriate. Besides, in the latent space, the label correlations will become implicit. To this end, we propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Moreover, we estimate the label correlations and infer model parameters simultaneously via the local geometric structure of the input to achieve mutual enhancement. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification. The exploited high-order label correlations are consistent with common sense empirically. Our code is publicly available at https://github.com/Chongjie-Si/HOMI.