Publication detail

Restricted Boltzmann Machine method for dimensionality reduction of large spectroscopic data

VRÁBEL, J. POŘÍZKA, P. KAISER, J.

Original Title

Restricted Boltzmann Machine method for dimensionality reduction of large spectroscopic data

Type

journal article in Web of Science

Language

English

Original Abstract

Multivariate data obtained using, for instance, Laser-Induced Breakdown Spectroscopy (LIBS), are quite bulky and complex. Advanced processing of spectroscopic data demands a multidisciplinary approach, covering not only modern machine learning tools but also a deep understanding of underlying physical mechanisms. Dimension reduction and visualization of large datasets is a task of significant interest in the spectroscopic data processing. Commonly employed linear techniques (e.g., Principal Component Analysis, PCA) cannot explain the correlations of higher-order which are present in the data. Even more, computational cost and memory limitations become way more relevant considering the size of “modern” LIBS data (millions of high-dimensional spectra). Methods based on Artificial Neural Networks (ANN) seem suitable for this task, and based on their success, they are given considerable attention within the spectroscopic community. We propose a new methodology based on Restricted Boltzmann Machine (ANN method) for dimensionality reduction of spectroscopic data and compare it to standard PCA. As an extension to successful reconstruction, we demonstrate a generation of new (unseen) spectra by the RBM model trained on a large spectroscopic dataset. This data generation is of great use not only for the extending measured datasets but also as a proper training state's confirmation of the model.

Keywords

Laser-Induced Breakdown Spectroscopy (LIBS), Spectroscopic data, Restricted Boltzmann Machine (RBM), Dimension reduction, Machine learning

Authors

VRÁBEL, J.; POŘÍZKA, P.; KAISER, J.

Released

6. 4. 2020

Publisher

Elsevier

ISBN

0584-8547

Periodical

Spectrochimica Acta Part B

Year of study

167

Number

105849

State

United Kingdom of Great Britain and Northern Ireland

Pages from

NA

Pages to

NA

Pages count

8

URL