Restricted Boltzmann Machine method for dimensionality reduction of large spectroscopic data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F20%3APU136191" target="_blank" >RIV/00216305:26620/20:PU136191 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0584854720300410?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0584854720300410?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.sab.2020.105849" target="_blank" >10.1016/j.sab.2020.105849</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Restricted Boltzmann Machine method for dimensionality reduction of large spectroscopic data

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Restricted Boltzmann Machine method for dimensionality reduction of large spectroscopic data

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10301 - Atomic, molecular and chemical physics (physics of atoms and molecules including collision, interaction with radiation, magnetic resonances, Mössbauer effect)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Spectrochimica Acta Part B

ISSN

0584-8547

e-ISSN

—

Svazek periodika

167

Číslo periodika v rámci svazku

105849

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

8

Strana od-do

„NA“-„NA“

Kód UT WoS článku

000535905500004

EID výsledku v databázi Scopus

2-s2.0-85082767921