2022/01/11 by Paul Irofti, Irofti, Paul, Cristián Rusu +3
Computer Science · Engineering · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Numerical Analysis (math.NA) #Sparse and Compressive Sensing Techniques #Speech and Audio Processing #Structural Health Monitoring Techniques
paper · pdf · doi:10.48550/arxiv.2201.03869
openalex publication_date 2022/01/11 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28
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.