# Unsupervised Dictionary Learning for Anomaly Detection

arXiv: https://arxiv.org/abs/2003.00293 · HTML: https://mlsanity.com/paper/2003.00293/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2020.

## Built on by (most cited first, 2 of 2)

- [Dictionary Learning with Uniform Sparse Representations for Anomaly Detection](https://mlsanity.com/paper/2201.03869/) (2022)
- [Comparative Study on Semi-supervised Learning Applied for Anomaly Detection in Hydraulic Condition Monitoring System](https://mlsanity.com/paper/2306.02709/) (2023)

## Abstract (arXiv)

We investigate the possibilities of employing dictionary learning to address the requirements of most anomaly detection applications, such as absence of supervision, online formulations, low false positive rates. We present new results of our recent semi-supervised online algorithm, TODDLeR, on a anti-money laundering application. We also introduce a novel unsupervised method of using the performance of the learning algorithm as indication of the nature of the samples.