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

Offered By: Chemometrics & Machine Learning in Copenhagen via YouTube

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Chemometrics Courses Machine Learning Courses Data Modeling Courses Mass Spectrometry Courses

Course Description

Overview

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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.

Syllabus

Shift-invariant trilinearity and soft shift-invariant trilinearity


Taught by

Chemometrics & Machine Learning in Copenhagen

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