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2018
Conference Paper
Titel
One-class Autoencoder approach to classify Raman spectra outliers
Abstract
We present an one-class Anomaly detector based on (deep) Autoencoder for Raman spectra. Omitting preprocessing of the spectra, we use raw data of our main class to learn the reconstruction, with many typical noise sources automatically reduced as the outcome. To separate anomalies from the norm class, we use several, independent statistical metrics for a majority voting. Our evaluation shows a f1-score of up to 99% success.