Working Paper Article Version 1 This version is not peer-reviewed

Sparse Coded Autoencoder Features for Chemometric Data Analysis

Version 1 : Received: 8 May 2019 / Approved: 9 May 2019 / Online: 9 May 2019 (11:31:46 CEST)

How to cite: Bilal, M.; Ullah, M. Sparse Coded Autoencoder Features for Chemometric Data Analysis. Preprints 2019, 2019050102 Bilal, M.; Ullah, M. Sparse Coded Autoencoder Features for Chemometric Data Analysis. Preprints 2019, 2019050102

Abstract

We proposed a 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 curse of dimensionality in chemometric data analysis. We introduce a novel technique of automatic selection of nodes inside hidden layer of an autoencoder through pareto optimization. Moreover, linear regression, ϵ-SVR , and Gaussian process regressor are 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 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.

Subject Areas

Chemometric data,sparse autoencoder, gaussian process regressor, pareto optimization.

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