Robust Classification of High-Dimensional Spectroscopy Data Using Deep Learning and Data Synthesis.

Journal of Chemical Information and Modeling
James HoustonMichael G Madden

Abstract

This paper presents a new approach to classification of high-dimensional spectroscopy data and demonstrates that it outperforms other current state-of-the art approaches. The specific task we consider is identifying whether samples contain chlorinated solvents or not, based on their Raman spectra. We also examine robustness to classification of outlier samples that are not represented in the training set (negative outliers). A novel application of a locally connected neural network (NN) for the binary classification of spectroscopy data is proposed and demonstrated to yield improved accuracy over traditionally popular algorithms. Additionally, we present the ability to further increase the accuracy of the locally connected NN algorithm through the use of synthetic training spectra, and we investigate the use of autoencoder based one-class classifiers and outlier detectors. Finally, a two-step classification process is presented as an alternative to the binary and one-class classification paradigms. This process combines the locally connected NN classifier, the use of synthetic training data, and an autoencoder based outlier detector to produce a model which is shown to both produce high classification accuracy and be robust in ...Continue Reading

References

Apr 20, 2006·Journal of Pharmaceutical and Biomedical Analysis·Jiewen ZhaoC H Fang
Dec 3, 2014·Neural Networks : the Official Journal of the International Neural Network Society·Jürgen Schmidhuber
Sep 28, 2017·Applied Radiation and Isotopes : Including Data, Instrumentation and Methods for Use in Agriculture, Industry and Medicine·Mati SheinfeldItzhak Orion
Feb 16, 2019·Scientific Reports·M Chatzidakis, G A Botton

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Citations

Jul 19, 2020·Optics Express·Rola HouhouThomas Bocklitz
Aug 17, 2020·Talanta·Camelia Berghian-Grosan, Dana Alina Magdas

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