{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T10:27:04Z","timestamp":1778840824286,"version":"3.51.4"},"reference-count":62,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T00:00:00Z","timestamp":1635897600000},"content-version":"vor","delay-in-days":306,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62062040"],"award-info":[{"award-number":["62062040"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102270"],"award-info":[{"award-number":["62102270"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62006174"],"award-info":[{"award-number":["62006174"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61967010"],"award-info":[{"award-number":["61967010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2019M661117"],"award-info":[{"award-number":["2019M661117"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009102","name":"Education Department of Jiangxi Province","doi-asserted-by":"publisher","award":["GJJ191709"],"award-info":[{"award-number":["GJJ191709"]}],"id":[{"id":"10.13039\/501100009102","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009102","name":"Education Department of Jiangxi Province","doi-asserted-by":"publisher","award":["GJJ191689"],"award-info":[{"award-number":["GJJ191689"]}],"id":[{"id":"10.13039\/501100009102","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2412019FZ049"],"award-info":[{"award-number":["2412019FZ049"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012135","name":"Jiangxi Normal University","doi-asserted-by":"publisher","award":["YJS2020045"],"award-info":[{"award-number":["YJS2020045"]}],"id":[{"id":"10.13039\/501100012135","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007620","name":"Department of Education of Liaoning Province","doi-asserted-by":"publisher","award":["JYT19040"],"award-info":[{"award-number":["JYT19040"]}],"id":[{"id":"10.13039\/501100007620","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>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\u2010norm\u2010based 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\u2010of\u2010the\u2010art methods, the proposed approach can achieve the best performance.<\/jats:p>","DOI":"10.1155\/2021\/4026132","type":"journal-article","created":{"date-parts":[[2021,11,4]],"date-time":"2021-11-04T05:35:08Z","timestamp":1636004108000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8741-8028","authenticated-orcid":false,"given":"Shicheng","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7338-7934","authenticated-orcid":false,"given":"Shumin","family":"Lai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9273-8381","authenticated-orcid":false,"given":"Yan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4075-3512","authenticated-orcid":false,"given":"Wenle","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9828-0319","authenticated-orcid":false,"given":"Yugen","family":"Yi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,11,3]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1145\/3381028"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/surv.2010.021510.00088"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2015.11.016"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2018.2877701"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-020-01196-2"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2013.05.022"},{"key":"e_1_2_9_7_2","doi-asserted-by":"crossref","unstructured":"BreunigM. M. KriegelH. P. NgR. T.et al. LOF: identifying density-based local outliers Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data May 2000 Dallas TX USA 93\u2013104.","DOI":"10.1145\/342009.335388"},{"key":"e_1_2_9_8_2","doi-asserted-by":"crossref","unstructured":"HautamakiV. KarkkainenI. andFrantiP. Outlier detection using k-nearest neighbour graph 3 Proceedings of the 17th International Conference on Pattern Recognition 2004 (ICPR 2004) 2004 Cambridge UK IEEE 430\u2013433.","DOI":"10.1109\/ICPR.2004.1334558"},{"key":"e_1_2_9_9_2","doi-asserted-by":"crossref","unstructured":"ZhangK. HutterM. andJinH. A new local distance-based outlier detection approach for scattered real-world data Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining April 2009 Bangkok Thailand Springer 813\u2013822.","DOI":"10.1007\/978-3-642-01307-2_84"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956758"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1002\/sam.11161"},{"key":"e_1_2_9_12_2","doi-asserted-by":"crossref","unstructured":"KriegelH. P. SchubertM. andZimekA. Angle-based outlier detection in high-dimensional data Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining August 2008 Las Vegas NV USA 444\u2013452.","DOI":"10.1145\/1401890.1401946"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106919"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.26599\/TST.2019.9010051"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2020.2982406"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2019.2944377"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2012.99"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2008.04.006"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2019.2893116"},{"key":"e_1_2_9_20_2","doi-asserted-by":"crossref","unstructured":"XiongL. ChenX. andSchneiderJ. Direct robust matrix factorizatoin for anomaly detection Proceedings of the 2011 IEEE 11th International Conference on Data Mining December 2011 Columbia Canada IEEE 844\u2013853.","DOI":"10.1109\/ICDM.2011.52"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2020.115984"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2018.2869271"},{"key":"e_1_2_9_23_2","doi-asserted-by":"crossref","unstructured":"TongH.andLinC. Y. Non-negative residual matrix factorization with application to graph anomaly detection Proceedings of the 2011 SIAM International Conference on Data Mining April 2011 Mesa Arizona USA 143\u2013153.","DOI":"10.1137\/1.9781611972818.13"},{"key":"e_1_2_9_24_2","doi-asserted-by":"crossref","unstructured":"KannanR. WooH. AggarwalC. C.et al. Outlier detection for text data Proceedings of the 2017 Siam International Conference on Data Mining April 2017 Houston TX USA 489\u2013497.","DOI":"10.1137\/1.9781611974973.55"},{"key":"e_1_2_9_25_2","doi-asserted-by":"crossref","unstructured":"AlshammariH. GhorbelO. AseeriM.et al. Non-negative matrix factorization (NMF) for outlier detection in Wireless Sensor Networks Proceedings of the 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC) June 2018 Limassol Cyprus IEEE 506\u2013511.","DOI":"10.1109\/IWCMC.2018.8450421"},{"key":"e_1_2_9_26_2","first-page":"1548","article-title":"Graph regularized nonnegative matrix factorization for data representation","volume":"33","author":"Cai D.","year":"2010","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_2_9_27_2","doi-asserted-by":"crossref","unstructured":"KuangD. DingC. andParkH. Symmetric nonnegative matrix factorization for graph clustering Proceedings of the 2012 SIAM International Conference on Data Mining April 2012 Anaheim CA USA Society for Industrial and Applied Mathematics 106\u2013117.","DOI":"10.1137\/1.9781611972825.10"},{"key":"e_1_2_9_28_2","first-page":"1","article-title":"Neighborhood structure assisted non-negative matrix factorization and its application in unsupervised point-wise anomaly detection","volume":"22","author":"Ahmed I.","year":"2021","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_9_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2015.2430359"},{"key":"e_1_2_9_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/tbme.2012.2217493"},{"key":"e_1_2_9_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107087"},{"key":"e_1_2_9_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.09.009"},{"key":"e_1_2_9_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.79"},{"key":"e_1_2_9_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.11.026"},{"key":"e_1_2_9_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106794"},{"key":"e_1_2_9_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2012.11.021"},{"key":"e_1_2_9_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/lsp.2015.2410031"},{"key":"e_1_2_9_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2015.2493201"},{"key":"e_1_2_9_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2872900"},{"key":"e_1_2_9_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2019.107320"},{"key":"e_1_2_9_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2010.2090535"},{"key":"e_1_2_9_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcsvt.2019.2892971"},{"key":"e_1_2_9_43_2","doi-asserted-by":"crossref","unstructured":"HeY. KavukcuogluK. WangY.et al. Unsupervised feature learning by deep sparse coding Proceedings of the 2014 SIAM International Conference on Data Mining April 2014 Philadelphia PA USA 902\u2013910.","DOI":"10.1137\/1.9781611973440.103"},{"key":"e_1_2_9_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/taslp.2017.2748240"},{"key":"e_1_2_9_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2016.2611583"},{"key":"e_1_2_9_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2017.2666220"},{"key":"e_1_2_9_47_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.03.006"},{"key":"e_1_2_9_48_2","doi-asserted-by":"crossref","unstructured":"ZhouC.andPaffenrothR. C. Anomaly detection with robust deep autoencoders Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining August 2017 Halifax Canada 665\u2013674.","DOI":"10.1145\/3097983.3098052"},{"key":"e_1_2_9_49_2","unstructured":"LiD. ChenD. GohJ.et al. Anomaly detection with generative adversarial networks for multivariate time series 2018 https:\/\/arxiv.org\/abs\/1809.04758."},{"key":"e_1_2_9_50_2","doi-asserted-by":"crossref","unstructured":"HeK. ZhangX. RenS.et al. Deep residual learning for image recognition Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition June 2016 Las Vegas NV USA 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_9_51_2","first-page":"801","article-title":"Efficient sparse coding algorithms","author":"Lee H.","year":"2007","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_9_52_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-015-0444-8"},{"key":"e_1_2_9_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2005.31"},{"key":"e_1_2_9_54_2","doi-asserted-by":"crossref","unstructured":"TangJ. ChenZ. FuA. W. C.et al. Enhancing effectiveness of outlier detections for low density patterns Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining May 2002 Taipei Taiwan Springer Berlin Heidelberg 535\u2013548.","DOI":"10.1007\/3-540-47887-6_53"},{"key":"e_1_2_9_55_2","doi-asserted-by":"crossref","unstructured":"KriegelH. P. Kr\u00f6gerP. SchubertE.et al. LoOP: local outlier probabilities Proceedings of the 18th ACM Conference on Information and Knowledge Management November 2009 Hong Kong China 1649\u20131652.","DOI":"10.1145\/1645953.1646195"},{"key":"e_1_2_9_56_2","doi-asserted-by":"crossref","unstructured":"JinW. TungA. K. H. HanJ.et al. Ranking outliers using symmetric neighborhood relationship Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining April 2006 Singapore Springer 577\u2013593.","DOI":"10.1007\/11731139_68"},{"key":"e_1_2_9_57_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.02.039"},{"key":"e_1_2_9_58_2","doi-asserted-by":"crossref","unstructured":"KriegelH. P. SchubertM. andZimekA. Angle-based outlier detection in high-dimensional data Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining August 2008 Las Vegas NV USA 444\u2013452.","DOI":"10.1145\/1401890.1401946"},{"key":"e_1_2_9_59_2","doi-asserted-by":"crossref","unstructured":"SchubertE. ZimekA. andKriegelH. P. Generalized outlier detection with flexible kernel density estimates Proceedings of the 2014 SIAM International Conference on Data Mining April 2014 Philadelphia PA USA Society for Industrial and Applied Mathematics 542\u2013550.","DOI":"10.1137\/1.9781611973440.63"},{"key":"e_1_2_9_60_2","unstructured":"YuW. ZengG. LuoP.et al. Embedding with autoencoder regularization Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases September 2013 Prague Czech Republic Springer Berlin Heidelberg 208\u2013223."},{"key":"e_1_2_9_61_2","unstructured":"ZongB. SongQ. MinM. R.et al. Deep autoencoding Gaussian mixture model for unsupervised anomaly detection Proceedings of the International Conference on Learning Representations April-May 2018 Vancouver Canada https:\/\/openreview.net\/forum?id=BJJLHbb0."},{"key":"e_1_2_9_62_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2018.2848198"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/4026132.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/4026132.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/4026132","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T11:23:42Z","timestamp":1722943422000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/4026132"}},"subtitle":[],"editor":[{"given":"Henry Man Fai","family":"Leung","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":62,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/4026132"],"URL":"https:\/\/doi.org\/10.1155\/2021\/4026132","archive":["Portico"],"relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-08-27","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-10-18","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-11-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"4026132"}}