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Volume: 31 | Article ID: art00005
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CHEMOMETRIC DATA ANALYSIS WITH AUTOENCODER NEURAL NETWORK
  DOI :  10.2352/ISSN.2470-1173.2019.1.VDA-679  Published OnlineJanuary 2019
Abstract

We propose novel deep learning based chemometric data analysis technique. We trained L2 regularized sparse autoencoder end-to-end for reducing the size of the feature vector to handle the classic problem of the curse of dimensionality in chemometric data analysis. We introduce a novel technique of automatic selection of nodes inside the hidden layer of an autoencoder through Pareto optimization. Moreover, Gaussian process regressor is applied on the reduced size feature vector for the regression. We evaluated our technique on orange juice and wine dataset and results are compared against 3 state-of-the-art methods. Quantitative results are shown on Normalized Mean Square Error (NMSE) and the results show considerable improvement in the state-of-the-art.

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Muhammad Bilal, Mohib Ullah, Habib Ullah, "CHEMOMETRIC DATA ANALYSIS WITH AUTOENCODER NEURAL NETWORKin Proc. IS&T Int’l. Symp. on Electronic Imaging: Visualization and Data Analysis,  2019,  pp 679-1 - 679-6,  https://doi.org/10.2352/ISSN.2470-1173.2019.1.VDA-679

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