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Poster
Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models
Tomoharu Iwata · Makoto Yamada

Tue Dec 06 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #166 #None
in Tuesday Posters »

We propose probabilistic latent variable models for multi-view anomaly detection, which is the task of finding instances that have inconsistent views given multi-view data. With the proposed model, all views of a non-anomalous instance are assumed to be generated from a single latent vector. On the other hand, an anomalous instance is assumed to have multiple latent vectors, and its different views are generated from different latent vectors. By inferring the number of latent vectors used for each instance with Dirichlet process priors, we obtain multi-view anomaly scores. The proposed model can be seen as a robust extension of probabilistic canonical correlation analysis for noisy multi-view data. We present Bayesian inference procedures for the proposed model based on a stochastic EM algorithm. The effectiveness of the proposed model is demonstrated in terms of performance when detecting multi-view anomalies.

Keywords: (Other) Unsupervised Learning Methods · Matrix Factorization · (Other) Probabilistic Models and Methods · Component Analysis (ICA,PCA,CCA, FLDA)
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Author Information

Tomoharu Iwata (NTT)
Makoto Yamada (Kyoto University)

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