# Fast Multi-Instance Multi-Label Learning

arXiv: https://arxiv.org/abs/1310.2049 · HTML: https://mlsanity.com/paper/1310.2049/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2013.

## Built on by (most cited first, 12 of 12)

- [Weakly Supervised Object Localization and Detection: A Survey](https://mlsanity.com/paper/2104.07918/) (2021)
- [Deep Learning for Micro-expression Recognition: A Survey](https://mlsanity.com/paper/2107.02823/) (2021)
- [Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport](https://mlsanity.com/paper/2104.08489/) (2021)
- [Heterogeneous Contrastive Learning](https://mlsanity.com/paper/2105.09401/) (2021)
- [Self-Paced Learning for Images of Antinuclear Antibodies](https://mlsanity.com/paper/2511.21519/) (2025)
- [Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classification](https://mlsanity.com/paper/2209.02625/) (2022)
- [Multi-label Ranking: Mining Multi-label and Label Ranking Data](https://mlsanity.com/paper/2101.00583/) (2021)
- [Multi-modal Data based Semi-Supervised Learning for Vehicle Positioning](https://mlsanity.com/paper/2410.20680/) (2024)
- [A Robust Multilabel Method Integrating Rule-based Transparent Model, Soft Label Correlation Learning and Label Noise Resistance](https://mlsanity.com/paper/2301.03283/) (2023)
- [Active Learning in Incomplete Label Multiple Instance Multiple Label Learning](https://mlsanity.com/paper/2107.10804/) (2021)
- [MetaMIML: Meta Multi-Instance Multi-Label Learning](https://mlsanity.com/paper/2111.04112/) (2021)
- [MIML library: a Modular and Flexible Library for Multi-instance Multi-label Learning](https://mlsanity.com/paper/2402.08056/) (2024)

## Abstract (arXiv)

In many real-world tasks, particularly those involving data objects with complicated semantics such as images and texts, one object can be represented by multiple instances and simultaneously be associated with multiple labels. Such tasks can be formulated as multi-instance multi-label learning (MIML) problems, and have been extensively studied during the past few years. Existing MIML approaches have been found useful in many applications; however, most of them can only handle moderate-sized data. To efficiently handle large data sets, in this paper we propose the MIMLfast approach, which first constructs a low-dimensional subspace shared by all labels, and then trains label specific linear models to optimize approximated ranking loss via stochastic gradient descent. Although the MIML problem is complicated, MIMLfast is able to achieve excellent performance by exploiting label relations with shared space and discovering sub-concepts for complicated labels. Experiments show that the performance of MIMLfast is highly competitive to state-of-the-art techniques, whereas its time cost is much less; particularly, on a data set with 20K bags and 180K instances, MIMLfast is more than 100 times faster than existing MIML approaches. On a larger data set where none of existing approaches can return results in 24 hours, MIMLfast takes only 12 minutes. Moreover, our approach is able to identify the most representative instance for each label, and thus providing a chance to understand the relation between input patterns and output label semantics.