Abstract
There have been multiple attempts to tackle the problem of identifying abnormal instances that have inconsistent behaviors in multi-view data (i.e., multi-view anomalies) but the problem still remains a challenge. In this paper, we propose an unsupervised approach with probabilistic latent variable models to detect multi-view anomalies in multi-view data. In our proposed model, we assume that views of an instance are generated from a shared latent variable that uniformly represents that instance. Since the latent variable is shared across views, an abnormal instance that exhibits inconsistencies across different views would have a lower likelihood. This is because, using a single latent variable, the model could not explain well all views that are inconsistent. Therefore, the likelihood of instances based on the proposed shared latent variable model can be used to detect multi-view anomalies. We derive a variational inference algorithm for learning the model parameters that scales well to large datasets. We compare our proposed method with several state-of-the-art methods for multi-view anomaly detection on several datasets. The results show that our method outperforms the existing methods in detecting multi-view anomalies.
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Notes
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Experimentally, non-linear neural networks work well for our problem in most of datasets. In the experiments, we use \(\mu _{\theta }^{(v)}(z_n)=\text {Linear}(\text {ReLU}(\text {Linear}(z_n)))\).
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In our experiments, \(\alpha =1\) and \(\sigma =0.001\) work well for most of the datasets.
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\(L=1\) works well in our experiments.
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We use Adam optimization algorithm.
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We use the implementations from scikit-learn.
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Acknowledgments
This research is sponsored by NSF #1757207 and NSF #1914635.
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Nguyen, P., Le, T.M.V. (2024). SeeM: A Shared Latent Variable Model for Unsupervised Multi-view Anomaly Detection. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14645. Springer, Singapore. https://doi.org/10.1007/978-981-97-2242-6_7
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