  Anomaly detection using dimensionality reduction has been an essential
technique for monitoring multidimensional data. Although deep learning-based
methods have been well studied for their remarkable detection performance,
their interpretability is still a problem. In this paper, we propose a novel
algorithm for estimating the dimensions contributing to the detected anomalies
by using variational autoencoders (VAEs). Our algorithm is based on an
approximative probabilistic model that considers the existence of anomalies in
the data, and by maximizing the log-likelihood, we estimate which dimensions
contribute to determining data as an anomaly. The experiments results with
benchmark datasets show that our algorithm extracts the contributing dimensions
more accurately than baseline methods.
