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Detail publikace
VRÁBEL, J. POŘÍZKA, P. KAISER, J.
Originální název
Restricted Boltzmann Machine Method for Dimensionality Reduction of Spectroscopic Data
Typ
konferenční sborník (ne článek)
Jazyk
angličtina
Originální abstrakt
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.
Klíčová slova
LIBS, Machine Learning, RBM, Neural Networks, Spectroscopy, Data, Dimension Reduction
Autoři
VRÁBEL, J.; POŘÍZKA, P.; KAISER, J.
Vydáno
8. 9. 2019
Nakladatel
Spektroskopická společnost Jana Marka Marci
Místo
Ke Karlovu 2027/3, 120 00 Praha 2 - Nové Město
ISBN
978-80-88195-13-9
Kniha
EMSLIBS 2019 Book of abstracts
Strany počet
293
URL
http://libs.ceitec.cz/files/281/213.pdf
BibTex
@proceedings{BUT159096, editor="Jakub {Vrábel} and Pavel {Pořízka} and Jozef {Kaiser}", title="Restricted Boltzmann Machine Method for Dimensionality Reduction of Spectroscopic Data", year="2019", pages="293", publisher="Spektroskopická společnost Jana Marka Marci", address="Ke Karlovu 2027/3, 120 00 Praha 2 - Nové Město", isbn="978-80-88195-13-9", url="http://libs.ceitec.cz/files/281/213.pdf" }