Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models

NeurIPS 2016 Tomoharu IwataMakoto Yamada

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... (read more)

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