  We propose a nonparametric Bayesian probabilistic latent variable model for
multi-view anomaly detection, which is the task of finding instances that have
inconsistent views. 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 and
imputing missing values in multi-view data with anomalies.
