Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models
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 multiview 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.
This paper provides an approach, based on PPCA and PCCA, to finding outlier observations in multi-view data sets. The model embeds observations in a latent space shared across views and finds projections from that latent space to each view. Observations that do not agree with the shared latent space and projection (common to all observations in a particular view) are recognized as outliers. The approach uses stochastic EM to approximate the random variables of the model conditioned on data. 
The paper presents an intriguing approach to outlier detection. Using the probability of the number of clusters being greater than 1 is an interesting innovation that I have not seen before. One limitation of that approach I see is whether to interpret instances with more than one latent vector as "outliers" or just multimodal. The description at the end of Section 5 bears this out: does the fact that a movie has more than one genre make it an outlier? In general, this means that the proposed method will work best when each instance can be represented with a single vector across multiple views.  Presentation was rated "sub-standard" mostly due to the very dense blocks of text in some places (e.g., the related work section). Also, it would be better to have subsections in the paper to structure the text more. The derivations starting line 99 needed more detail for how the marginal distributions were arrived at (perhaps a large chunk of this part can be pushed to the appendix?).  The experiments covered a large number of data sets but the only meaningful benchmark was PCCA (and SVM for one experiment). I thought the method of creating anomalous instances was clever and could be useful to others. The paragraph starting line 219 describes results for multi-view anomaly detection but it would be helpful in pointing toward specific data sets in the evaluation when doing this. The purpose of the paragraph starting line 238 was not clear to me, I could not see why it was needed.   I noticed a typo on line 89 (missing space).