Dictionary Learning with Uniform Sparse Representations for Anomaly Detection
This work addresses anomaly detection in signal processing applications, but it appears incremental as it builds on existing dictionary learning methods.
The paper tackled the problem of detecting abnormal samples in datasets by using a dictionary learning formulation that seeks uniform sparse representations to identify the underlying subspace of the majority of data points, with numerical simulations showing efficient discrimination of anomalies.
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.