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Contrastive representation learning for spectroscopy data analysis

Artem Pavlovich Vorozhtsov 1
Artem Pavlovich Vorozhtsov
, 
Polina V Kitina 1
Polina V Kitina
1 Department of Fundamental Physical and Chemical Engineering, M.V. Lomonosov Moscow State University, Moscow, Russian Federation
10.1016/j.mencom.2024.10.006
Published 2024-10-22
Communication , Volume 34, Issue 6, 786-787
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Vorozhtsov A. P., Kitina P. V. Contrastive representation learning for spectroscopy data analysis // Mendeleev Communications. 2024. Vol. 34. No. 6. pp. 786-787.
GOST all authors (up to 50) Copy
Vorozhtsov A. P., Kitina P. V. Contrastive representation learning for spectroscopy data analysis // Mendeleev Communications. 2024. Vol. 34. No. 6. pp. 786-787.
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TY - JOUR
DO - 10.1016/j.mencom.2024.10.006
UR - https://mendcomm.colab.ws/publications/10.1016/j.mencom.2024.10.006
TI - Contrastive representation learning for spectroscopy data analysis
T2 - Mendeleev Communications
AU - Vorozhtsov, Artem Pavlovich
AU - Kitina, Polina V
PY - 2024
DA - 2024/10/22
PB - Mendeleev Communications
SP - 786-787
IS - 6
VL - 34
ER -
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@article{2024_Vorozhtsov,
author = {Artem Pavlovich Vorozhtsov and Polina V Kitina},
title = {Contrastive representation learning for spectroscopy data analysis},
journal = {Mendeleev Communications},
year = {2024},
volume = {34},
publisher = {Mendeleev Communications},
month = {Oct},
url = {https://mendcomm.colab.ws/publications/10.1016/j.mencom.2024.10.006},
number = {6},
pages = {786--787},
doi = {10.1016/j.mencom.2024.10.006}
}
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Vorozhtsov, Artem Pavlovich, and Polina V Kitina. “Contrastive representation learning for spectroscopy data analysis.” Mendeleev Communications, vol. 34, no. 6, Oct. 2024, pp. 786-787. https://mendcomm.colab.ws/publications/10.1016/j.mencom.2024.10.006.

Keywords

machine learning
metric learning
neural network
representation learning
spectra analysis.
spectroscopy

Abstract

Metric-based representation learning showed good accuracy in identifying objects from one-dimensional spectroscopy data, robustness to small dataset size and the ability to change the data domain without fine-tuning.

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