# Dictionary Learning with Uniform Sparse Representations for Anomaly Detection

arXiv: https://arxiv.org/abs/2201.03869 · HTML: https://mlsanity.com/paper/2201.03869/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 22 references that are in the map.
- First posted on arXiv 11 Jan 2022.

## Builds on (most cited first, 1 of 1)

- [Unsupervised Dictionary Learning for Anomaly Detection](https://mlsanity.com/paper/2003.00293/) (2020)

## Built on by (most cited first, 1 of 1)

- [Fusing Dictionary Learning and Support Vector Machines for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2404.04064/) (2024)

## Abstract (arXiv)

Many applications like audio and image processing show that sparse representations are a powerful and efficient signal modeling technique. Finding an optimal dictionary that generates at the same time the sparsest representations of data and the smallest approximation error is a hard problem approached by dictionary learning (DL). We study how DL performs in detecting abnormal samples in a dataset of signals. In this paper we use a particular DL formulation that seeks uniform sparse representations model to detect the underlying subspace of the majority of samples in a dataset, using a K-SVD-type algorithm. Numerical simulations show that one can efficiently use this resulted subspace to discriminate the anomalies over the regular data points.