Multi-view Anomaly Detection via Probabilistic Latent Variable Models

TL;DR

This paper introduces a Bayesian probabilistic model for detecting anomalies in multi-view data by modeling instances with either single or multiple latent vectors, improving anomaly detection and missing data imputation.

Contribution

It presents a nonparametric Bayesian latent variable model that infers the number of latent vectors per instance for robust multi-view anomaly detection.

Findings

Effective in detecting multi-view anomalies

Improves missing value imputation in noisy multi-view data

Demonstrates superior performance over existing methods

Abstract

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.