{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T03:18:52Z","timestamp":1779333532944,"version":"3.51.4"},"reference-count":17,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T00:00:00Z","timestamp":1707782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2023YFB2703700"],"award-info":[{"award-number":["2023YFB2703700"]}]},{"name":"Key-Area Research and Development Program of Shandong Province","award":["2021CXGC010108"],"award-info":[{"award-number":["2021CXGC010108"]}]},{"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"}]},{"name":"Guangzhou Science and Technology Program","award":["2023A04J0314"],"award-info":[{"award-number":["2023A04J0314"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"<jats:p>With the development of multi-view learning, multi-view outlier detection has received increasing attention in recent years. However, the current research still faces two challenges: (1) The current research lacks theoretical analysis tools for multi-view outliers. (2) Most current multi-view outlier detection algorithms are based on shallow structural assumptions of the data, such as cluster assumptions and subspace assumptions, thus they are not suitable for more complex data distributions. In addressing these two issues, this article proposes three occurrence mechanisms of multi-view outlier, which serve as foundational theoretical analysis tools for multi-view outliers. Utilizing proposed mechanisms, we analyze the impact of multi-view outliers and the information structure of multi-view data and validate our findings through experiments. Finally, we propose a novel algorithm referred to as Information-Aware Multi-View Outlier Detection (IAMOD). In contrast to other methods, IAMOD focuses on the information structure of multi-view data without relying on shallow structural assumptions. By learning a compact representation of the sample that is semantically rich and non-redundant, IAMOD can accurately identify multi-view outliers by comparing the consistency of the representations\u2019 neighbors and views. Extensive experimental results demonstrate that our approach outperforms several state-of-the-art multi-view outlier detection methods.<\/jats:p>","DOI":"10.1145\/3638354","type":"journal-article","created":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T11:31:29Z","timestamp":1703244689000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Information-aware Multi-view Outlier Detection"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4165-7773","authenticated-orcid":false,"given":"Jinrong","family":"Lai","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2244-197X","authenticated-orcid":false,"given":"Tong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7048-3445","authenticated-orcid":false,"given":"Chuan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7878-4330","authenticated-orcid":false,"given":"Zibin","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Sun Yat-sen University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,2,13]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"Learning representations by maximizing mutual information across views","volume":"32","author":"Bachman Philip","year":"2019","unstructured":"Philip Bachman, R. Devon Hjelm, and William Buchwalter. 2019. Learning representations by maximizing mutual information across views. Advances in Neural Information Processing Systems 32 (2019).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_3_2","first-page":"531","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Belghazi Mohamed Ishmael","year":"2018","unstructured":"Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm. 2018. Mutual information neural estimation. In Proceedings of the International Conference on Machine Learning. PMLR, 531\u2013540."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16873"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339670"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2011.16"},{"key":"e_1_3_2_8_2","unstructured":"R. Devon Hjelm Alex Fedorov Samuel Lavoie-Marchildon Karan Grewal Phil Bachman Adam Trischler and Yoshua Bengio. 2018. Learning deep representations by mutual information estimation and maximization. arXiv:1808.06670. Retrieved from https:\/\/arxiv.org\/abs\/1808.06670"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00136"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014057"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11826"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3168363"},{"key":"e_1_3_2_13_2","first-page":"1545","volume-title":"Proceedings of the 22nd ACM International Conference on Information and Knowledge Management","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. 1545\u20131548."},{"key":"e_1_3_2_14_2","unstructured":"Aaron van den Oord Yazhe Li and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv:1807.03748. Retrieved from https:\/\/arxiv.org\/abs\/1807.03748"},{"key":"e_1_3_2_15_2","unstructured":"Karthik Sridharan and Sham M. Kakade. 2008. An information theoretic framework for multi-view learning. (2008)."},{"key":"e_1_3_2_16_2","first-page":"6827","article-title":"What makes for good views for contrastive learning?","volume":"33","author":"Tian Yonglong","year":"2020","unstructured":"Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola. 2020. What makes for good views for contrastive learning? Advances in Neural Information Processing Systems 33 (2020), 6827\u20136839.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_17_2","article-title":"A self-representation method with local similarity preserving for fast multi-view outlier detection","author":"Wang Yu","year":"2022","unstructured":"Yu Wang, Chuan Chen, Jinrong Lai, Lele Fu, Yuren Zhou, and Zibin Zheng. 2022. A self-representation method with local similarity preserving for fast multi-view outlier detection. ACM Transactions on Knowledge Discovery from Data (2022).","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2754942"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3638354","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3638354","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:50:42Z","timestamp":1750287042000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3638354"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,13]]},"references-count":17,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,5,31]]}},"alternative-id":["10.1145\/3638354"],"URL":"https:\/\/doi.org\/10.1145\/3638354","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,13]]},"assertion":[{"value":"2023-05-22","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-12-15","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-02-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}