# Linear-time One-Class Classification with Repeated Element-wise Folding

arXiv: https://arxiv.org/abs/2408.11412 · HTML: https://mlsanity.com/paper/2408.11412/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## Builds on (most cited first, 8 of 8)

- [DROCC: Deep Robust One-Class Classification](https://mlsanity.com/paper/2002.12718/) (2020)
- [On the folded normal distribution](https://mlsanity.com/paper/1402.3559/) (2014)
- [Subspace Support Vector Data Description](https://mlsanity.com/paper/1802.03989/) (2018)
- [Multimodal Subspace Support Vector Data Description](https://mlsanity.com/paper/1904.07698/) (2019)
- [Graph-Embedded Subspace Support Vector Data Description](https://mlsanity.com/paper/2104.14370/) (2021)
- [Boosting rare benthic macroinvertebrates taxa identification with one-class classification](https://mlsanity.com/paper/2002.10420/) (2020)
- [Generalized Reference Kernel for One-class Classification](https://mlsanity.com/paper/2205.00534/) (2022)
- [On The Construction of Extreme Learning Machine for Online and Offline One-Class Classification - An Expanded Toolbox](https://mlsanity.com/paper/1701.04516/) (2017)

## Abstract (arXiv)

This paper proposes an easy-to-use method for one-class classification: Repeated Element-wise Folding (REF). The algorithm consists of repeatedly standardizing and applying an element-wise folding operation on the one-class training data. Equivalent mappings are performed on unknown test items and the classification prediction is based on the item's distance to the origin of the final distribution. As all the included operations have linear time complexity, the proposed algorithm provides a linear-time alternative for the commonly used computationally much more demanding approaches. Furthermore, REF can avoid the challenges of hyperparameter setting in one-class classification by providing robust default settings. The experiments show that the proposed method can produce similar classification performance or even outperform the more complex algorithms on various benchmark datasets. Matlab codes for REF are publicly available at https://github.com/JenniRaitoharju/REF.