# Accelerating Extreme Classification via Adaptive Feature Agglomeration

arXiv: https://arxiv.org/abs/1905.11769 · HTML: https://mlsanity.com/paper/1905.11769/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2019.

## Built on by (most cited first, 7 of 7)

- [ECLARE: Extreme Classification with Label Graph Correlations](https://mlsanity.com/paper/2108.00261/) (2021)
- [Learning with Holographic Reduced Representations](https://mlsanity.com/paper/2109.02157/) (2021)
- [A Survey on Extreme Multi-label Learning](https://mlsanity.com/paper/2210.03968/) (2022)
- [A Walsh Hadamard Derived Linear Vector Symbolic Architecture](https://mlsanity.com/paper/2410.22669/) (2024)
- [MatchXML: An Efficient Text-label Matching Framework for Extreme Multi-label Text Classification](https://mlsanity.com/paper/2308.13139/) (2023)
- [Learning Structured Representations by Embedding Class Hierarchy with Fast Optimal Transport](https://mlsanity.com/paper/2410.03052/) (2024)
- [Light-weight Deep Extreme Multilabel Classification](https://mlsanity.com/paper/2304.11045/) (2023)

## Abstract (arXiv)

Extreme classification seeks to assign each data point, the most relevant labels from a universe of a million or more labels. This task is faced with the dual challenge of high precision and scalability, with millisecond level prediction times being a benchmark. We propose DEFRAG, an adaptive feature agglomeration technique to accelerate extreme classification algorithms. Despite past works on feature clustering and selection, DEFRAG distinguishes itself in being able to scale to millions of features, and is especially beneficial when feature sets are sparse, which is typical of recommendation and multi-label datasets. The method comes with provable performance guarantees and performs efficient task-driven agglomeration to reduce feature dimensionalities by an order of magnitude or more. Experiments show that DEFRAG can not only reduce training and prediction times of several leading extreme classification algorithms by as much as 40%, but also be used for feature reconstruction to address the problem of missing features, as well as offer superior coverage on rare labels.