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SUMMARY:Multi-view Anomaly Detection via Robust Probabilistic Latent Varia
 ble Models  - Tomoharu Iwata - Learning and Intelligent Systems Research G
 roup of NTT Communication Science Laboratories\, Kyoto\, Japan
DTSTART:20161103T110000Z
DTEND:20161103T120000Z
UID:TALK68876@talks.cam.ac.uk
CONTACT:Louise Segar
DESCRIPTION:We propose probabilistic latent variable models for multi-view
  anomaly detection\, which is the task of finding instances that have inco
 nsistent views given multi-view data. With the proposed model\, all views 
 of a non-anomalous instance are assumed to be generated from a single late
 nt vector. On the other hand\, an anomalous instance is assumed to have mu
 ltiple latent vectors\, and its different views are generated from differe
 nt latent vectors. By inferring the number of latent vectors used for each
  instance with Dirichlet process priors\, we obtain multi-view anomaly sco
 res. The proposed model can be seen as a robust extension of probabilistic
  canonical correlation analysis for noisy multi-view data. We present Baye
 sian inference procedures for the proposed model based on a stochastic EM 
 algorithm. The effectiveness of the proposed model is demonstrated in term
 s of performance when detecting multi-view anomalies. 
LOCATION:CBL Room BE-438
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