# Review of Extreme Multilabel Classification

arXiv: https://arxiv.org/abs/2302.05971 · HTML: https://mlsanity.com/paper/2302.05971/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2023.

## Built on by (most cited first, 5 of 5)

- [Stable-DiffCoder: Pushing the Frontier of Code Diffusion Large Language Model](https://mlsanity.com/paper/2601.15892/) (2026)
- [Angle based dynamic learning rate for gradient descent](https://mlsanity.com/paper/2304.10457/) (2023)
- [Light-weight Deep Extreme Multilabel Classification](https://mlsanity.com/paper/2304.11045/) (2023)
- [Tile Compression and Embeddings for Multi-Label Classification in GeoLifeCLEF 2024](https://mlsanity.com/paper/2407.06326/) (2024)
- [Large Language Models as Medical Codes Selectors: a benchmark using the International Classification of Primary Care](https://mlsanity.com/paper/2507.14681/) (2025)

## Abstract (arXiv)

Extreme multi-label classification or XMLC, is an active area of interest in machine learning. Compared to traditional multi-label classification, here the number of labels is extremely large, hence, the name extreme multi-label classification. Using classical one-versus-all classification does not scale in this case due to large number of labels; the same is true for any other classifier. Embedding labels and features into a lower-dimensional space is a common first step in many XMLC methods. Moreover, other issues include existence of head and tail labels, where tail labels are those that occur in a relatively small number of samples. The existence of tail labels creates issues during embedding. This area has invited application of wide range of approaches ranging from bit compression motivated from compressed sensing, tree based embeddings, deep learning based latent space embedding including using attention weights, linear algebra based embeddings such as SVD, clustering, hashing, to name a few. The community has come up with a useful set of metrics to identify correctly the prediction for head or tail labels.