Distance of spectroscopic data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F20%3APU137604" target="_blank" >RIV/00216305:26620/20:PU137604 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Distance of spectroscopic data

Popis výsledku v původním jazyce

Machine learning (ML) techniques are essential in a wide variety of modern spectroscopic applications. The majority of ML models use some form of distance computation. In the case of supervised learning, we may need to compute the distance of unknown spectra to the labeled representatives to decide the class correspondence. Also, in unsupervised learning, reconstruction error is considered (e.g. autoencoders), where distance is computed. One of the most prominent properties of spectroscopic data is high-dimensionality, sparsity and redundancy. [1] Thus, we are dealing with the curse of dimensionality (COD) in the processing of such data. It is a well-known [2] consequence of COD, that standardly utilized euclidean metric is behaving poorly in high-dimensional spaces. In the present work, we are studying alternative metrics to measure the distance of spectroscopic data and discuss resulting improvements in the performance of ML models. References: [1] Vrábel, J., Pořízka, P., & Kaiser, J. (2020). Res

Název v anglickém jazyce

Distance of spectroscopic data

Popis výsledku anglicky

Machine learning (ML) techniques are essential in a wide variety of modern spectroscopic applications. The majority of ML models use some form of distance computation. In the case of supervised learning, we may need to compute the distance of unknown spectra to the labeled representatives to decide the class correspondence. Also, in unsupervised learning, reconstruction error is considered (e.g. autoencoders), where distance is computed. One of the most prominent properties of spectroscopic data is high-dimensionality, sparsity and redundancy. [1] Thus, we are dealing with the curse of dimensionality (COD) in the processing of such data. It is a well-known [2] consequence of COD, that standardly utilized euclidean metric is behaving poorly in high-dimensional spaces. In the present work, we are studying alternative metrics to measure the distance of spectroscopic data and discuss resulting improvements in the performance of ML models. References: [1] Vrábel, J., Pořízka, P., & Kaiser, J. (2020). Res

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10406 - Analytical chemistry

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

2020

Kód důvěrnosti údajů

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