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PARASIAS: A new method for analyzing higher-order tensors with shifting profiles
Huiwen Yu1, Rasmus Bro1, Neal B Gallagher2
1Chemometrics Group, University of Copenhagen, Rolighedsvej 26, 1958, Frederiksberg C, Denmark.
Analytica Chimica Acta
|December 4, 2022
Summary
A new method, PARASIAS, efficiently analyzes shifted higher-order tensor data, outperforming PARAFAC2 in speed and robustness. It offers model simplicity and faster convergence for complex datasets like GC-MS.
Area of Science:
- Multivariate data analysis
- Chemometrics
- Spectroscopy
Background:
- Higher-order tensor analysis is crucial for complex datasets, including multi-way GC-MS.
- Retention time shifts in elution profiles violate assumptions of traditional tensor models.
Purpose of the Study:
- Introduce PARASIAS, a novel method for analyzing shifted higher-order tensor data.
- Evaluate PARASIAS's performance against the state-of-the-art PARAFAC2 model.
Main Methods:
- PARASIAS combines spectral transformation with PARAFAC modeling.
- The method was validated using simulated and real multi-way datasets.
Main Results:
- PARASIAS demonstrated significantly faster fitting times: 13x on simulated and 8x+ on real data compared to PARAFAC2.
- PARASIAS offers advantages in model simplicity, convergence speed, and robustness to data shifts.
- The model allows non-negativity constraints on the shift mode and extension to multiple shift modes.
Conclusions:
- PARASIAS is a faster and more robust alternative for analyzing shifted higher-order tensor data.
- While generally effective, PARASIAS may require more components than PARAFAC2 for data with >3 components without additional factors.
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