Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection

Anomaly detection (AD) aims to distinguish the data points that are inconsistent with the overall pattern of the data. Recently, unsupervised anomaly detection methods have aroused huge attention. Among these methods, feature representation (FR) plays an important role, which can directly affect the...

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Autores principales:	Shicheng Li, Shumin Lai, Yan Jiang, Wenle Wang, Yugen Yi
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/ac3346f90fa2471b96cc71428fea995b
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spelling	oai:doaj.org-article:ac3346f90fa2471b96cc71428fea995b2021-11-15T01:20:04ZGraph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection1687-527310.1155/2021/4026132https://doaj.org/article/ac3346f90fa2471b96cc71428fea995b2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4026132https://doaj.org/toc/1687-5273Anomaly detection (AD) aims to distinguish the data points that are inconsistent with the overall pattern of the data. Recently, unsupervised anomaly detection methods have aroused huge attention. Among these methods, feature representation (FR) plays an important role, which can directly affect the performance of anomaly detection. Sparse representation (SR) can be regarded as one of matrix factorization (MF) methods, which is a powerful tool for FR. However, there are some limitations in the original SR. On the one hand, it just learns the shallow feature representations, which leads to the poor performance for anomaly detection. On the other hand, the local geometry structure information of data is ignored. To address these shortcomings, a graph regularized deep sparse representation (GRDSR) approach is proposed for unsupervised anomaly detection in this work. In GRDSR, a deep representation framework is first designed by extending the single layer MF to a multilayer MF for extracting hierarchical structure from the original data. Next, a graph regularization term is introduced to capture the intrinsic local geometric structure information of the original data during the process of FR, making the deep features preserve the neighborhood relationship well. Then, a L1-norm-based sparsity constraint is added to enhance the discriminant ability of the deep features. Finally, a reconstruction error is applied to distinguish anomalies. In order to demonstrate the effectiveness of the proposed approach, we conduct extensive experiments on ten datasets. Compared with the state-of-the-art methods, the proposed approach can achieve the best performance.Shicheng LiShumin LaiYan JiangWenle WangYugen YiHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Shicheng Li Shumin Lai Yan Jiang Wenle Wang Yugen Yi Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection
description	Anomaly detection (AD) aims to distinguish the data points that are inconsistent with the overall pattern of the data. Recently, unsupervised anomaly detection methods have aroused huge attention. Among these methods, feature representation (FR) plays an important role, which can directly affect the performance of anomaly detection. Sparse representation (SR) can be regarded as one of matrix factorization (MF) methods, which is a powerful tool for FR. However, there are some limitations in the original SR. On the one hand, it just learns the shallow feature representations, which leads to the poor performance for anomaly detection. On the other hand, the local geometry structure information of data is ignored. To address these shortcomings, a graph regularized deep sparse representation (GRDSR) approach is proposed for unsupervised anomaly detection in this work. In GRDSR, a deep representation framework is first designed by extending the single layer MF to a multilayer MF for extracting hierarchical structure from the original data. Next, a graph regularization term is introduced to capture the intrinsic local geometric structure information of the original data during the process of FR, making the deep features preserve the neighborhood relationship well. Then, a L1-norm-based sparsity constraint is added to enhance the discriminant ability of the deep features. Finally, a reconstruction error is applied to distinguish anomalies. In order to demonstrate the effectiveness of the proposed approach, we conduct extensive experiments on ten datasets. Compared with the state-of-the-art methods, the proposed approach can achieve the best performance.
format	article
author	Shicheng Li Shumin Lai Yan Jiang Wenle Wang Yugen Yi
author_facet	Shicheng Li Shumin Lai Yan Jiang Wenle Wang Yugen Yi
author_sort	Shicheng Li
title	Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection
title_short	Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection
title_full	Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection
title_fullStr	Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection
title_full_unstemmed	Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection
title_sort	graph regularized deep sparse representation for unsupervised anomaly detection
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/ac3346f90fa2471b96cc71428fea995b
work_keys_str_mv	AT shichengli graphregularizeddeepsparserepresentationforunsupervisedanomalydetection AT shuminlai graphregularizeddeepsparserepresentationforunsupervisedanomalydetection AT yanjiang graphregularizeddeepsparserepresentationforunsupervisedanomalydetection AT wenlewang graphregularizeddeepsparserepresentationforunsupervisedanomalydetection AT yugenyi graphregularizeddeepsparserepresentationforunsupervisedanomalydetection
_version_	1718428942176616448

Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection

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