Shift-Invariant Trilinearity and Soft Shift-Invariant Trilinearity in GC-MS Data Analysis

Explore a groundbreaking method for shift-invariant non-negative tensor factorization in the analysis of GC-MS data in this 45-minute conference talk. Discover a fast alternative to the Parallel Factor Analysis 2 (PARAFAC2) model, designed to resolve co-eluting peaks and extract peak areas and clean mass spectra. Learn about the extension of this method to a more flexible soft-shift invariant tri-linearity algorithm, which has the potential to model shifts and shape changes of elution profiles across samples. Gain insights into advanced chemometrics and machine learning techniques that can revolutionize the analysis of complex chemical data.