{"type":"ScholarlyArticle","title":"Towards a Hierarchical Bayesian Model of Multi-View Anomaly Detection","authors":[{"name":"Zhen Wang","slug":"zhen-wang","given":"Zhen","family":"Wang","affiliation":"Department of Computer Science , University of Wyoming , WY , USA"},{"name":"Chao Lan","slug":"chao-lan","given":"Chao","family":"Lan","affiliation":"Department of Computer Science , University of Wyoming , WY , USA"}],"abstract":"Traditional anomaly detectors examine a single view of instances and cannot discover multi-view anomalies, i.e., instances that exhibit inconsistent behaviors across different views. To tackle the problem, several multi-view anomaly detectors have been developed recently, but they are all transductive and unsupervised thus may suffer some challenges. In this paper, we propose a novel inductive semi-supervised Bayesian multi-view anomaly detector. Specifically, we first present a generative model for normal data. Then, we build a hierarchical Bayesian model, by first assigning priors to all parameters and latent variables, and then assigning priors over the priors. Finally, we employ variational inference to approximate the posterior of the model and evaluate anomalous scores of multi-view instances. In the experiment, we show the proposed Bayesian detector consistently outperforms state-of-the-art counterparts across several public data sets and three well-known types of multi-view anomalies. In theory, we prove the inferred Bayesian estimator is consistent and derive a proximate sample complexity for the proposed anomaly detector.","doi":"10.24963/ijcai.2020/335","url":"https://ijcai.org/proceedings/2020/335","pdf":"https://ijcai.org/proceedings/2020/0335.pdf","markdown":"https://ijcai.org/proceedings/2020/335.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/335","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":335,"pages":{"first":2420,"last":2426},"keywords":["Data Mining: Other","Machine Learning: Multi-instance;Multi-label;Multi-view learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25512/file_25512.m3u8","length_sec":278,"provider":"hls"}],"references":[{"position":1,"raw":"[Akcay et al., 2018] Samet Akcay, Amir Atapour- Abarghouei, and Toby P Breckon. 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