Analysis and Classification of Frequency-Domain Terahertz Reflection Spectra Using Supervised and Unsupervised Dimensionality Reduction Methods
| Autor: | Pawel P. Cielecki, Mathias Hedegaard Kristensen, Esben Skovsen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Terahertz Data visualization Machine learning Preprocessor Electrical and Electronic Engineering Instrumentation Spectroscopy THz screening Radiation THz-FDS business.industry Dimensionality reduction Pattern recognition Condensed Matter Physics Linear discriminant analysis Classification Statistical classification Identification (information) Frequency-domain ComputingMethodologies_PATTERNRECOGNITION Principal component analysis THz spectroscopy Artificial intelligence business Curse of dimensionality |
| Zdroj: | Cielecki, P P, Kristensen, M H & Skovsen, E 2021, ' Analysis and Classification of Frequency-Domain Terahertz Reflection Spectra Using Supervised and Unsupervised Dimensionality Reduction Methods ', Journal of Infrared, Millimeter and Terahertz Waves, vol. 42, no. 9-10, pp. 1005-1026 . https://doi.org/10.1007/s10762-021-00810-w |
| DOI: | 10.1007/s10762-021-00810-w |
| Popis: | The unique properties of terahertz (THz) spectroscopy show a great potential for security and defense applications such as safe screening of persons and objects. However, a successful implementation of THz screening systems requires a development of reliable and efficient identification algorithms. Dimensionality reduction (DR) methods aim to reduce the dimensionality of the multivariate data and are therefore commonly used as a preprocessing step for classification algorithms and as an analytical tool allowing data visualization. In this paper, we compare the use of unsupervised and supervised DR methods for analysis and classification of THz reflection spectra based on their most widespread linear representatives, namely principal component analysis and linear discriminant analysis, respectively. To this end, both methods were applied to more than 5000 THz reflection spectra acquired from six active materials mixed at three different concentrations with polyethylene and measured at various humidity conditions. While considering scenarios with different levels of complexity, we found that the supervised approach provides better results because it enables efficient grouping despite intra-class variability. Furthermore, we showed that manipulating labels introduced into the supervised DR algorithm allows conditioning the data for a desired classification task such as security screening. Presented classification results show that simple machine learning algorithms are sufficient for highly accurate classification (>98.6%) of THz spectra, which will be suitable for many real-life applications of THz spectroscopy based on material identification. |
| Databáze: | OpenAIRE |
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