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• W2899694519 abstract "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." @default.
• W2899694519 created "2018-11-16" @default.
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• W2899694519 date "2018-11-12" @default.
• W2899694519 modified "2023-09-27" @default.
• W2899694519 title "Estimation of Dimensions Contributing to Detected Anomalies with Variational Autoencoders." @default.
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• W2899694519 hasPublicationYear "2018" @default.
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