Restricted Boltzmann Machine Method for Dimensionality Reduction of Spectroscopic Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F19%3APU133565" target="_blank" >RIV/00216305:26620/19:PU133565 - isvavai.cz</a>

Výsledek na webu

<a href="http://libs.ceitec.cz/files/281/213.pdf" target="_blank" >http://libs.ceitec.cz/files/281/213.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Restricted Boltzmann Machine Method for Dimensionality Reduction of 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. Strong non-linearities of those mechanisms are inducing problems in their processing using standard linear algorithms. 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 well known linear techniques such as PCA. Moreover, we apply this technique to the processing and mapping of very high-dimensional LIBS data.

Název v anglickém jazyce

Restricted Boltzmann Machine Method for Dimensionality Reduction of 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. Strong non-linearities of those mechanisms are inducing problems in their processing using standard linear algorithms. 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 well known linear techniques such as PCA. Moreover, we apply this technique to the processing and mapping of very high-dimensional LIBS data.

Druh

O - Ostatní výsledky

CEP obor

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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)

Rok uplatnění

2019

Kód důvěrnosti údajů

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