Distance of spectroscopic data

Result code in 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>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Distance of spectroscopic data
Original language description
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
Czech name
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Czech description
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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
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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