Restricted Boltzmann Machine Method for Dimensionality Reduction of Spectroscopic Data

Result code in 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>

Result on the web

<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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Result language

angličtina

Original language name

Restricted Boltzmann Machine Method for Dimensionality Reduction of Spectroscopic Data

Original language description

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.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

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

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

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