Dictionary Learning with Uniform Sparse Representations for Anomaly Detection

• URL: http://arxiv.org/abs/2201.03869v1
• Date: Tue, 11 Jan 2022 10:22:46 GMT
• Title: Dictionary Learning with Uniform Sparse Representations for Anomaly Detection
• Authors: Paul Irofti, Cristian Rusu, Andrei P\u{a}tra\c{s}cu
• Abstract summary: We study how dictionary learning (DL) performs in detecting abnormal samples in a dataset of signals. Numerical simulations show that one can efficiently use this resulted subspace to discriminate the anomalies over the regular data points.
• Score: 2.277447144331876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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