# A Flexible Framework for Anomaly Detection via Dimensionality Reduction

arXiv: https://arxiv.org/abs/1909.04060 · HTML: https://mlsanity.com/paper/1909.04060/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2019.

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

- [Exploiting Autoencoder's Weakness to Generate Pseudo Anomalies](https://mlsanity.com/paper/2405.05886/) (2024)
- [Efficient Generation of Hidden Outliers for Improved Outlier Detection](https://mlsanity.com/paper/2402.03846/) (2024)

## Abstract (arXiv)

Anomaly detection is challenging, especially for large datasets in high dimensions. Here we explore a general anomaly detection framework based on dimensionality reduction and unsupervised clustering. We release DRAMA, a general python package that implements the general framework with a wide range of built-in options. We test DRAMA on a wide variety of simulated and real datasets, in up to 3000 dimensions, and find it robust and highly competitive with commonly-used anomaly detection algorithms, especially in high dimensions. The flexibility of the DRAMA framework allows for significant optimization once some examples of anomalies are available, making it ideal for online anomaly detection, active learning and highly unbalanced datasets.