Dictionary Learning with Uniform Sparse Representations for Anomaly Detection
Abstract
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.
Cite
@article{arxiv.2201.03869,
title = {Dictionary Learning with Uniform Sparse Representations for Anomaly Detection},
author = {Paul Irofti and Cristian Rusu and Andrei Pătraşcu},
journal= {arXiv preprint arXiv:2201.03869},
year = {2022}
}