A deep learning approach for analyzing the composition of chemometric data

TL;DR

This paper introduces a deep learning method combining autoencoders with Pareto optimization and Gaussian process regression to improve chemometric data analysis, effectively reducing feature dimensionality and enhancing predictive accuracy.

Contribution

The paper presents a novel autoencoder node selection technique via Pareto optimization and integrates it with Gaussian process regression for chemometric data analysis.

Findings

Significant reduction in feature vector size.

Improved NMSE compared to state-of-the-art methods.

Effective handling of high-dimensional chemometric data.

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.