Project detail

Distance of spectroscopic data

Duration: 01.02.2021 — 31.01.2023

Funding resources

Evropská unie - Interní grantová soutěž

- whole funder (2021-02-01 - 2023-01-31)

On the project

The problem of the distance metric for high-dimensional spectra is addressed. Distance (or similarity) measurement is a key component of numerous machine learning algorithms. By default, the Euclidean distance is dominantly used, which is known as a poor metric for high-dimensional data. Overlooking the importance of metric selection leads to counterintuitive results and limited performance. By implementing alternative metrics, a rapid boost of performance and model interpretability is expected with a high potential to influence the community.

Mark

CEITEC-K-21-6978

Default language

Czech

People responsible

Vrábel Jakub, Ing. - principal person responsible

Units

Advanced instrumentation and methods for material characterization
- (2021-02-01 - 2023-01-31)
Central European Institute of Technology BUT
- (2021-02-01 - 2023-01-31)

Results

VRÁBEL, J.; KÉPEŠ, E.; POŘÍZKA, P.; KAISER, J. Physics-informed ML models for processing of spectroscopic data. 2021.
Detail

VRÁBEL, J.; KÉPEŠ, E.; POŘÍZKA, P.; KAISER, J. Artificial neural network weights penalization and initialization for spectroscopic data. 2021.
Detail

VRÁBEL, J. Physics-informed ML models for processing of spectroscopic data. 2021.
Detail

VRÁBEL, J.; KÉPEŠ, E.; NEDĚLNÍK, P.; BUDAY, J.; CEMPÍREK, J.; POŘÍZKA, P.; KAISER, J. Spectral library transfer between distinct Laser-Induced Breakdown Spectroscopy systems trained on simultaneous measurements. Journal of Analytical Atomic Spectrometry, 2023, vol. 38, no. 4, p. 841-853. ISSN: 1364-5544.
Detail

VRÁBEL, J.; KÉPEŠ, E.; NEDĚLNÍK, P.; POŘÍZKA, P.; KAISER, J. Spectra transfer between distinct LIBS systems using shared standards and machine learning. 2022.
Detail

VRÁBEL, J.; KÉPEŠ, E.; POŘÍZKA, P.; KAISER, J. Distance of Spectroscopic Data. 2022.
Detail

VRÁBEL, J.; KÉPEŠ, E.; POŘÍZKA, P.; KAISER, J. Towards interpretability of ANNs for spectroscopic data: inductive bias, lottery tickets, and input optimization. 2022.
Detail

VRÁBEL, J.; KÉPEŠ, E.; POŘÍZKA, P.; KAISER, J. Artificial Neural Networks for Classification. In Chemometrics and Numerical Methods in LIBS. 1. 2022. p. 213-240. ISBN: 978-1-119-75958-4.
Detail

Responsibility: Vrábel Jakub, Ing.