Dictionary Learning with Uniform Sparse Representations for Anomaly Detection

Author name not available (Why is that?)

Publication date: 11 January 2022

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.

Has companion code repository: https://github.com/pirofti/ad-usr-dl

This page was built for publication: Dictionary Learning with Uniform Sparse Representations for Anomaly Detection

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6387953)