{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T21:13:47Z","timestamp":1762809227629,"version":"3.41.0"},"reference-count":58,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T00:00:00Z","timestamp":1675123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2020B010165003"],"award-info":[{"award-number":["2020B010165003"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62176269"],"award-info":[{"award-number":["62176269"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2019A1515011043"],"award-info":[{"award-number":["2019A1515011043"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2023,1,31]]},"abstract":"<jats:p>With the rapidly growing attention to multi-view data in recent years, multi-view outlier detection has become a rising field with intense research. These researches have made some success, but still exist some issues that need to be solved. First, many multi-view outlier detection methods can only handle datasets that conform to the cluster structure but are powerless for complex data distributions such as manifold structures. This overly restrictive data assumption limits the applicability of these methods. In addition, almost the majority of multi-view outlier detection algorithms cannot solve the online detection problem of multi-view outliers. To address these issues, we propose a new detection method based on the local similarity relation and data reconstruction, i.e., the Self-Representation Method with Local Similarity Preserving for fast multi-view outlier detection (SRLSP). By using the local similarity structure, the proposed method fully utilizes the characteristics of outliers and detects outliers with an applicable objective function. Besides, a well-designed optimization algorithm is proposed, which completes each iteration with linear time complexity and can calculate each instance parallelly. Also, the optimization algorithm can be easily extended to the online version, which is more suitable for practical production environments. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed method on both performance and time complexity.<\/jats:p>","DOI":"10.1145\/3532191","type":"journal-article","created":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T11:56:58Z","timestamp":1651060618000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["A Self-Representation Method with Local Similarity Preserving for Fast Multi-View Outlier Detection"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6192-0087","authenticated-orcid":false,"given":"Yu","family":"Wang","sequence":"first","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7048-3445","authenticated-orcid":false,"given":"Chuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4165-7773","authenticated-orcid":false,"given":"Jinrong","family":"Lai","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5304-0434","authenticated-orcid":false,"given":"Lele","family":"Fu","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0497-0835","authenticated-orcid":false,"given":"Yuren","family":"Zhou","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7878-4330","authenticated-orcid":false,"given":"Zibin","family":"Zheng","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2023,3,15]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-14142-8","volume-title":"Data Mining","author":"Aggarwal Charu C.","year":"2015","unstructured":"Charu C. Aggarwal. 2015. Data Mining. Springer."},{"key":"e_1_3_2_3_2","first-page":"577","volume-title":"Proceedings of the IEEE Conference on Industrial Electronics and Applications (ICIEA)","author":"Ahmed Mohiuddin","year":"2013","unstructured":"Mohiuddin Ahmed and Abdun Naser Mahmood. 2013. A novel approach for outlier detection and clustering improvement. In Proceedings of the IEEE Conference on Industrial Electronics and Applications (ICIEA). 577\u2013582."},{"key":"e_1_3_2_4_2","first-page":"220","volume-title":"Proceedings of the IEEE\/IAFE Computational Intelligence for Financial Engineering (CIFEr)","author":"Aleskerov Emin","year":"1997","unstructured":"Emin Aleskerov, Bernd Freisleben, and Bharat Rao. 1997. CARDWATCH: A neural network based database mining system for credit card fraud detection. In Proceedings of the IEEE\/IAFE Computational Intelligence for Financial Engineering (CIFEr). 220\u2013226."},{"issue":"2","key":"e_1_3_2_5_2","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/TKDE.2006.29","article-title":"Distance-based detection and prediction of outliers","volume":"18","author":"Angiulli Fabrizio","year":"2005","unstructured":"Fabrizio Angiulli, Stefano Basta, and Clara Pizzuti. 2005. Distance-based detection and prediction of outliers. IEEE Transactions on Knowledge and Data Engineering (TKDE) 18, 2 (2005), 145\u2013160.","journal-title":"IEEE Transactions on Knowledge and Data Engineering (TKDE)"},{"key":"e_1_3_2_6_2","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/0-387-25465-X_7","volume-title":"Proceedings of the Data Mining and Knowledge Discovery Handbook","author":"Ben-Gal Irad","year":"2005","unstructured":"Irad Ben-Gal. 2005. Outlier detection. In Proceedings of the Data Mining and Knowledge Discovery Handbook. Springer, 131\u2013146."},{"key":"e_1_3_2_7_2","first-page":"1","article-title":"A novel method of spectral clustering in attributed networks by constructing parameter-free affinity matrix","author":"Berahmand Kamal","year":"2021","unstructured":"Kamal Berahmand, Mehrnoush Mohammadi, Azadeh Faroughi, and Rojiar Pir Mohammadiani. 2021. A novel method of spectral clustering in attributed networks by constructing parameter-free affinity matrix. Cluster Computing (2021), 1\u201320.","journal-title":"Cluster Computing"},{"key":"e_1_3_2_8_2","doi-asserted-by":"crossref","first-page":"104933","DOI":"10.1016\/j.compbiomed.2021.104933","article-title":"Spectral clustering on protein-protein interaction networks via constructing affinity matrix using attributed graph embedding","volume":"138","author":"Berahmand Kamal","year":"2021","unstructured":"Kamal Berahmand, Elahe Nasiri, and Yuefeng Li2021. Spectral clustering on protein-protein interaction networks via constructing affinity matrix using attributed graph embedding. Computers in Biology and Medicine 138 (2021), 104933.","journal-title":"Computers in Biology and Medicine"},{"issue":"1","key":"e_1_3_2_9_2","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1007\/s10791-011-9172-x","article-title":"Graph-based term weighting for information retrieval","volume":"15","author":"Blanco Roi","year":"2012","unstructured":"Roi Blanco and Christina Lioma. 2012. Graph-based term weighting for information retrieval. Information Retrieval 15, 1 (2012), 54\u201392.","journal-title":"Information Retrieval"},{"issue":"22","key":"e_1_3_2_10_2","doi-asserted-by":"crossref","first-page":"8637","DOI":"10.1016\/j.eswa.2015.07.018","article-title":"A practical outlier detection approach for mixed-attribute data","volume":"42","author":"Bouguessa Mohamed","year":"2015","unstructured":"Mohamed Bouguessa. 2015. A practical outlier detection approach for mixed-attribute data. Expert Systems with Applications 42, 22 (2015), 8637\u20138649.","journal-title":"Expert Systems with Applications"},{"key":"e_1_3_2_11_2","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511804441","volume-title":"Convex Optimization","author":"Boyd Stephen","year":"2004","unstructured":"Stephen Boyd and Lieven Vandenberghe. 2004. Convex Optimization. Cambridge university press."},{"key":"e_1_3_2_12_2","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1145\/342009.335388","volume-title":"Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data","author":"Breunig Markus M.","year":"2000","unstructured":"Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, and J\u00f6rg Sander. 2000. LOF: Identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. 93\u2013104."},{"key":"e_1_3_2_13_2","first-page":"2598","volume-title":"Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI)","author":"Cai Xiao","year":"2013","unstructured":"Xiao Cai, Feiping Nie, and Heng Huang. 2013. Multi-view k-means clustering on big data. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI). 2598\u20132604."},{"issue":"3","key":"e_1_3_2_14_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1970392.1970395","article-title":"Robust principal component analysis?","volume":"58","author":"Cand\u00e8s Emmanuel J.","year":"2011","unstructured":"Emmanuel J. Cand\u00e8s, Xiaodong Li, Yi Ma, and John Wright. 2011. Robust principal component analysis?Journal of the ACM (JACM) 58, 3 (2011), 1\u201337.","journal-title":"Journal of the ACM (JACM)"},{"key":"e_1_3_2_15_2","doi-asserted-by":"crossref","first-page":"106518","DOI":"10.1016\/j.knosys.2020.106518","article-title":"Robust multi-view k-means clustering with outlier removal","volume":"210","author":"Chen Chuan","year":"2020","unstructured":"Chuan Chen, Yu Wang, Weibo Hu, and Zibin Zheng. 2020. Robust multi-view k-means clustering with outlier removal. Knowledge-Based Systems (KBS) 210 (2020), 106518.","journal-title":"Knowledge-Based Systems (KBS)"},{"key":"e_1_3_2_16_2","first-page":"2144","volume-title":"Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI)","author":"Chen Zitai","year":"2019","unstructured":"Zitai Chen, Chuan Chen, Zong Zhang, Zibin Zheng, and Qingsong Zou. 2019. Variational graph embedding and clustering with laplacian eigenmaps. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI). 2144\u20132150."},{"key":"e_1_3_2_17_2","doi-asserted-by":"crossref","unstructured":"Li Cheng Yijie Wang and Xinwang Liu. 2021. Neighborhood consensus networks for unsupervised multi-view outlier detection. (2021).","DOI":"10.1609\/aaai.v35i8.16873"},{"issue":"3","key":"e_1_3_2_18_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3389547","article-title":"Robust unsupervised cross-modal hashing for multimedia retrieval","volume":"38","author":"Cheng Miaomiao","year":"2020","unstructured":"Miaomiao Cheng, Liping Jing, and Michael K. Ng. 2020. Robust unsupervised cross-modal hashing for multimedia retrieval. ACM Transactions on Information Systems (TOIS) 38, 3 (2020), 1\u201325.","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"issue":"11","key":"e_1_3_2_19_2","doi-asserted-by":"crossref","first-page":"2765","DOI":"10.1109\/TPAMI.2013.57","article-title":"Sparse subspace clustering: Algorithm, theory, and applications","volume":"35","author":"Elhamifar Ehsan","year":"2013","unstructured":"Ehsan Elhamifar and Ren\u00e9 Vidal. 2013. Sparse subspace clustering: Algorithm, theory, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 35, 11 (2013), 2765\u20132781.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)"},{"key":"e_1_3_2_20_2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.patrec.2017.03.008","article-title":"K-means clustering with outlier removal","volume":"90","author":"Gan Guojun","year":"2017","unstructured":"Guojun Gan and Michael Kwok-Po Ng. 2017. K-means clustering with outlier removal. Pattern Recognition Letters 90 (2017), 8\u201314.","journal-title":"Pattern Recognition Letters"},{"key":"e_1_3_2_21_2","first-page":"1050","volume-title":"Proceedings of the IEEE International Conference on Data Mining (ICDM)","author":"Gao Jing","year":"2011","unstructured":"Jing Gao, Wei Fan, Deepak Turaga, Srinivasan Parthasarathy, and Jiawei Han. 2011. A spectral framework for detecting inconsistency across multi-source object relationships. In Proceedings of the IEEE International Conference on Data Mining (ICDM). 1050\u20131055."},{"key":"e_1_3_2_22_2","first-page":"387","volume-title":"Proceedings of the 4th IEEE International Conference on Data Mining (ICDM\u201904)","author":"Ghoting Amol","year":"2004","unstructured":"Amol Ghoting, Matthew Eric Otey, and Srinivasan Parthasarathy. 2004. Loaded: Link-based outlier and anomaly detection in evolving data sets. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM\u201904). IEEE, 387\u2013390."},{"key":"e_1_3_2_23_2","doi-asserted-by":"crossref","DOI":"10.1007\/978-94-015-3994-4","volume-title":"Identification of Outliers","author":"Hawkins Douglas M.","year":"1980","unstructured":"Douglas M. Hawkins. 1980. Identification of Outliers. Springer."},{"issue":"3","key":"e_1_3_2_24_2","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1007\/s10115-012-0534-5","article-title":"Multi-domain anomaly detection in spatial datasets","volume":"36","author":"Janeja Vandana P.","year":"2013","unstructured":"Vandana P. Janeja and Revathi Palanisamy. 2013. Multi-domain anomaly detection in spatial datasets. Knowledge and Information Systems (KAIS) 36, 3 (2013), 749\u2013788.","journal-title":"Knowledge and Information Systems (KAIS)"},{"key":"e_1_3_2_25_2","first-page":"1132","volume-title":"Proceedings of the IEEE International Conference on Data Mining (ICDM)","author":"Ji Yu-Xuan","year":"2019","unstructured":"Yu-Xuan Ji, Ling Huang, Heng-Ping He, Chang-Dong Wang, Guangqiang Xie, Wei Shi, and Kun-Yu Lin. 2019. Multi-view outlier detection in deep intact space. In Proceedings of the IEEE International Conference on Data Mining (ICDM). 1132\u20131137."},{"key":"e_1_3_2_26_2","doi-asserted-by":"crossref","first-page":"105102","DOI":"10.1016\/j.knosys.2019.105102","article-title":"Multi-graph fusion for multi-view spectral clustering","volume":"189","author":"Kang Zhao","year":"2020","unstructured":"Zhao Kang, Guoxin Shi, Shudong Huang, Wenyu Chen, Xiaorong Pu, Joey Tianyi Zhou, and Zenglin Xu. 2020. Multi-graph fusion for multi-view spectral clustering. Knowledge-Based Systems (KBS) 189 (2020), 105102.","journal-title":"Knowledge-Based Systems (KBS)"},{"issue":"2","key":"e_1_3_2_27_2","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/s10618-009-0148-z","article-title":"A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributes","volume":"20","author":"Koufakou Anna","year":"2010","unstructured":"Anna Koufakou and Michael Georgiopoulos. 2010. A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributes. Data Mining and Knowledge Discovery 20, 2 (2010), 259\u2013289.","journal-title":"Data Mining and Knowledge Discovery"},{"issue":"10","key":"e_1_3_2_28_2","first-page":"1","article-title":"Parallel and distributed computing for cybersecurity","volume":"6","author":"Kumar Vipin","year":"2005","unstructured":"Vipin Kumar. 2005. Parallel and distributed computing for cybersecurity. IEEE Distributed Systems Online 6, 10 (2005), 1\u20139.","journal-title":"IEEE Distributed Systems Online"},{"key":"e_1_3_2_29_2","first-page":"3522","volume-title":"Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI)","author":"Li Kai","year":"2018","unstructured":"Kai Li, Sheng Li, Zhengming Ding, Weidong Zhang, and Yun Fu. 2018. Latent discriminant subspace representations for multi-view outlier detection. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI). 3522\u20133529."},{"issue":"3","key":"e_1_3_2_30_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3168363","article-title":"Multi-view low-rank analysis with applications to outlier detection","volume":"12","author":"Li Sheng","year":"2018","unstructured":"Sheng Li, Ming Shao, and Yun Fu. 2018. Multi-view low-rank analysis with applications to outlier detection. ACM Transactions on Knowledge Discovery from Data (TKDD) 12, 3 (2018), 1\u201322.","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"},{"key":"e_1_3_2_31_2","first-page":"1204","volume-title":"Proceedings of the IEEE International Conference on Data Mining (ICDM)","author":"Liang Youwei","year":"2019","unstructured":"Youwei Liang, Dong Huang, and Chang-Dong Wang. 2019. Consistency meets inconsistency: A unified graph learning framework for multi-view clustering. In Proceedings of the IEEE International Conference on Data Mining (ICDM). 1204\u20131209."},{"issue":"7","key":"e_1_3_2_32_2","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1109\/TKDE.2013.108","article-title":"An efficient approach for outlier detection with imperfect data labels","volume":"26","author":"Liu Bo","year":"2013","unstructured":"Bo Liu, Yanshan Xiao, S. Yu Philip, Zhifeng Hao, and Longbing Cao. 2013. An efficient approach for outlier detection with imperfect data labels. IEEE Transactions on Knowledge and Data Engineering (TKDE) 26, 7 (2013), 1602\u20131616.","journal-title":"IEEE Transactions on Knowledge and Data Engineering (TKDE)"},{"issue":"6","key":"e_1_3_2_33_2","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1109\/TKDE.2019.2954317","article-title":"Clustering with outlier removal","volume":"33","author":"Liu Hongfu","year":"2021","unstructured":"Hongfu Liu, Jun Li, Yue Wu, and Yun Fu. 2021. Clustering with outlier removal. IEEE Transactions on Knowledge and Data Engineering (TKDE) 33, 6 (2021), 2369\u20132379.","journal-title":"IEEE Transactions on Knowledge and Data Engineering (TKDE)"},{"key":"e_1_3_2_34_2","first-page":"347","volume-title":"Proceedings of the 12th European Conference on Computer Vision (ECCV)","volume":"7578","author":"Lu Can-Yi","year":"2012","unstructured":"Can-Yi Lu, Hai Min, Zhong-Qiu Zhao, Lin Zhu, De-Shuang Huang, and Shuicheng Yan. 2012. Robust and efficient subspace segmentation via least squares regression. In Proceedings of the 12th European Conference on Computer Vision (ECCV), Vol. 7578. 347\u2013360."},{"key":"e_1_3_2_35_2","first-page":"1545","volume-title":"Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (CIKM)","author":"Alvarez Alejandro Marcos","year":"2013","unstructured":"Alejandro Marcos Alvarez, Makoto Yamada, Akisato Kimura, and Tomoharu Iwata. 2013. Clustering-based anomaly detection in multi-view data. In Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (CIKM). 1545\u20131548."},{"key":"e_1_3_2_36_2","first-page":"2564","volume-title":"Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI)","author":"Nie Feiping","year":"2017","unstructured":"Feiping Nie, Jing Li, and Xuelong Li2017. Self-weighted multiview clustering with multiple graphs. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI). 2564\u20132570."},{"key":"e_1_3_2_37_2","first-page":"977","volume-title":"Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)","author":"Nie Feiping","year":"2014","unstructured":"Feiping Nie, Xiaoqian Wang, and Heng Huang. 2014. Clustering and projected clustering with adaptive neighbors. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). 977\u2013986."},{"issue":"2","key":"e_1_3_2_38_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3439950","article-title":"Deep learning for anomaly detection: A review","volume":"54","author":"Pang Guansong","year":"2021","unstructured":"Guansong Pang, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel. 2021. Deep learning for anomaly detection: A review. ACM Computing Surveys (CSUR) 54, 2 (2021), 1\u201338.","journal-title":"ACM Computing Surveys (CSUR)"},{"issue":"5500","key":"e_1_3_2_39_2","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","article-title":"Nonlinear dimensionality reduction by locally linear embedding","volume":"290","author":"Roweis Sam T.","year":"2000","unstructured":"Sam T. Roweis and Lawrence K. Saul. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290, 5500 (2000), 2323\u20132326.","journal-title":"Science"},{"key":"e_1_3_2_40_2","article-title":"A unifying review of deep and shallow anomaly detection","author":"Ruff Lukas","year":"2021","unstructured":"Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Gr\u00e9goire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, and Klaus-Robert M\u00fcller. 2021. A unifying review of deep and shallow anomaly detection. Proceedings of the IEEE (2021).","journal-title":"Proceedings of the IEEE"},{"key":"e_1_3_2_41_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Ruff Lukas","year":"2020","unstructured":"Lukas Ruff, Robert A. Vandermeulen, Nico G\u00f6rnitz, Alexander Binder, Emmanuel M\u00fcller, Klaus-Robert M\u00fcller, and Marius Kloft. 2020. Deep semi-supervised anomaly detection. In Proceedings of the International Conference on Learning Representations. Retrieved from https:\/\/openreview.net\/forum?id=HkgH0TEYwH."},{"key":"e_1_3_2_42_2","first-page":"8861","volume-title":"Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"Sattler Felix","year":"2020","unstructured":"Felix Sattler, Klaus-Robert M\u00fcller, Thomas Wiegand, and Wojciech Samek. 2020. On the byzantine robustness of clustered federated learning. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 8861\u20138865."},{"key":"e_1_3_2_43_2","first-page":"582","volume-title":"Proceedings of the Advances in Neural Information Processing Systems (NIPS)","volume":"12","author":"Sch\u00f6lkopf Bernhard","year":"1999","unstructured":"Bernhard Sch\u00f6lkopf, Robert C. Williamson, Alexander J. Smola, John Shawe-Taylor, and John C. Platt1999. Support vector method for novelty detection. In Proceedings of the Advances in Neural Information Processing Systems (NIPS), Vol. 12. 582\u2013588."},{"key":"e_1_3_2_44_2","first-page":"4894","volume-title":"Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI)","author":"Sheng Xiang-Rong","year":"2019","unstructured":"Xiang-Rong Sheng, De-Chuan Zhan, Su Lu, and Yuan Jiang. 2019. Multi-view anomaly detection: Neighborhood in locality matters. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI). 4894\u20134901."},{"key":"e_1_3_2_45_2","first-page":"3","volume-title":"Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA)","author":"Spence Clay","year":"2001","unstructured":"Clay Spence, Lucas Parra, and Paul Sajda. 2001. Detection, synthesis and compression in mammographic image analysis with a hierarchical image probability model. In Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA). 3\u201310."},{"key":"e_1_3_2_46_2","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.neunet.2018.06.017","article-title":"Improved multi-view privileged support vector machine","volume":"106","author":"Tang Jingjing","year":"2018","unstructured":"Jingjing Tang, Yingjie Tian, Xiaohui Liu, Dewei Li, Jia Lv, and Gang Kou. 2018. Improved multi-view privileged support vector machine. Neural Networks 106 (2018), 96\u2013109.","journal-title":"Neural Networks"},{"key":"e_1_3_2_47_2","first-page":"1121","volume-title":"Proceedings of the 24th International Conference on Pattern Recognition (ICPR)","author":"Wang Chu","year":"2018","unstructured":"Chu Wang, Yan-Ming Zhang, and Cheng-Lin Liu. 2018. Anomaly detection via minimum likelihood generative adversarial networks. In Proceedings of the 24th International Conference on Pattern Recognition (ICPR). 1121\u20131126."},{"key":"e_1_3_2_48_2","first-page":"352","volume-title":"Proceedings of the 30th International Conference on Machine Learning (ICML)","volume":"28","author":"Wang Hua","year":"2013","unstructured":"Hua Wang, Feiping Nie, and Heng Huang. 2013. Multi-view clustering and feature learning via structured sparsity. In Proceedings of the 30th International Conference on Machine Learning (ICML), Vol. 28. 352\u2013360."},{"key":"e_1_3_2_49_2","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1016\/j.knosys.2018.10.022","article-title":"A study of graph-based system for multi-view clustering","volume":"163","author":"Wang Hao","year":"2019","unstructured":"Hao Wang, Yan Yang, Bing Liu, and Hamido Fujita. 2019. A study of graph-based system for multi-view clustering. Knowledge-Based Systems (KBS) 163 (2019), 1009\u20131019.","journal-title":"Knowledge-Based Systems (KBS)"},{"key":"e_1_3_2_50_2","doi-asserted-by":"crossref","unstructured":"Zhiyue Wu Hongzuo Xu Guansong Pang Fengyuan Yu Yijie Wang Songlei Jian and Yongjun Wang. 2021. Dram failure prediction in aiops: Empirical evaluation challenges and opportunities. arXiv:2104.15052. Retrieved from https:\/\/arxiv.org\/abs\/2104.15052.","DOI":"10.1109\/JCC53141.2021.00012"},{"issue":"12","key":"e_1_3_2_51_2","first-page":"2401","article-title":"Multi-view support vector machines with the consensus and complementarity information","volume":"32","author":"Xie Xijiong","year":"2019","unstructured":"Xijiong Xie and Shiliang Sun. 2019. Multi-view support vector machines with the consensus and complementarity information. IEEE Transactions on Knowledge and Data Engineering (TKDE) 32, 12 (2019), 2401\u20132413.","journal-title":"IEEE Transactions on Knowledge and Data Engineering (TKDE)"},{"key":"e_1_3_2_52_2","unstructured":"Chang Xu Dacheng Tao and Chao Xu. 2013. A survey on multi-view learning. arXiv:1304.5634. Retrieved from https:\/\/arxiv.org\/abs\/1304.5634."},{"key":"e_1_3_2_53_2","first-page":"1408","volume-title":"Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM)","author":"Xu Hongzuo","year":"2019","unstructured":"Hongzuo Xu, Yijie Wang, Yongjun Wang, and Zhiyue Wu. 2019. Mix: A joint learning framework for detecting both clustered and scattered outliers in mixed-type data. In Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 1408\u20131413."},{"key":"e_1_3_2_54_2","doi-asserted-by":"crossref","first-page":"107874","DOI":"10.1016\/j.patcog.2021.107874","article-title":"Mean-shift outlier detection and filtering","volume":"115","author":"Yang Jiawei","year":"2021","unstructured":"Jiawei Yang, Susanto Rahardja, and Pasi Fr\u00e4nti. 2021. Mean-shift outlier detection and filtering. Pattern Recognition 115 (2021), 107874.","journal-title":"Pattern Recognition"},{"key":"e_1_3_2_55_2","unstructured":"Fanghua Ye Zhiwei Lin Chuan Chen Zibin Zheng Hong Huang and Emine Yilmaz. 2020. Outlier resilient collaborative web service QoS prediction. arXiv:2006.01287. Retrieved from https:\/\/arxiv.org\/abs\/2006.01287."},{"key":"e_1_3_2_56_2","first-page":"5634","volume-title":"Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI)","author":"Zadeh Amir","year":"2018","unstructured":"Amir Zadeh, Paul Pu Liang, Navonil Mazumder, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency. 2018. Memory fusion network for multi-view sequential learning. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI). 5634\u20135641."},{"key":"e_1_3_2_57_2","first-page":"4077","volume-title":"Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI)","author":"Zhao Handong","year":"2015","unstructured":"Handong Zhao and Yun Fu. 2015. Dual-regularized multi-view outlier detection. In Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI). 4077\u20134083."},{"issue":"1","key":"e_1_3_2_58_2","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1109\/TIP.2017.2754942","article-title":"Consensus regularized multi-view outlier detection","volume":"27","author":"Zhao Handong","year":"2017","unstructured":"Handong Zhao, Hongfu Liu, Zhengming Ding, and Yun Fu. 2017. Consensus regularized multi-view outlier detection. IEEE Transactions on Image Processing (TIP) 27, 1 (2017), 236\u2013248.","journal-title":"IEEE Transactions on Image Processing (TIP)"},{"key":"e_1_3_2_59_2","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.inffus.2017.02.007","article-title":"Multi-view learning overview: Recent progress and new challenges","volume":"38","author":"Zhao Jing","year":"2017","unstructured":"Jing Zhao, Xijiong Xie, Xin Xu, and Shiliang Sun. 2017. Multi-view learning overview: Recent progress and new challenges. Information Fusion 38 (2017), 43\u201354.","journal-title":"Information Fusion"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3532191","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3532191","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:42Z","timestamp":1750183782000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3532191"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,31]]},"references-count":58,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1,31]]}},"alternative-id":["10.1145\/3532191"],"URL":"https:\/\/doi.org\/10.1145\/3532191","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2023,1,31]]},"assertion":[{"value":"2021-09-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-04-14","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-03-15","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}