1 research outputs found
A deep learning approach for analyzing the composition of chemometric data
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.Comment: 6 pages, 1 figure, 1 tabl