Lovelace's Square - Shift-Invariant Tri-linear Tensor model (SIT)

The SIT (Shift-Invariant Tri-linear Tensor model) algorithm decomposes complex tensor datasets, particularly from gas chromatography-mass spectrometry (GC-MS). It offers a 20-60x speed advantage over the latest PARAFAC2 implementations, allows constraints across all modes during optimization, and requires fewer latent variables than existing methods while achieving robust real-world performance.

Reference: Schneide, P. A.; Bro, R.; Gallagher, N. B. (2023). Shift-invariant tri-linearity: A new model for resolving untargeted gas chromatography coupled mass spectrometry data. Journal of Chemometrics, 37(8), e3501. https://doi.org/10.1002/cem.3501

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Shift-Invariant Tri-linear Tensor model (SIT)

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Shift-Invariant Tri-linear Tensor model (SIT)

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