Applied Multi-way Analysis

Course Description

Applied Multi-way Analysis begins with a review of multi-way data structures and the instruments that generate such data. Multi-way models, such as PARAFAC, Tucker and N-PLS, will be illustrated. Emphasis will be placed on how to apply these models and to show their unique properties. Applications, such as sensory data, chromatographic data and fluorescence data will be discussed. The course will finish with some advanced considerations such as optimal preprocessing and missing data. The course includes hands-on computer time for participants to work example problems using PLS_Toolbox and MATLAB.

Prerequisites

Linear Algebra for Machine Learning and Chemometrics, MATLAB for Machine Learning and Chemometrics, and Chemometrics I–PCA or equivalent experience.

Course Outline

1. Introduction
   Notation used in multi-way analysis
   Intro to 3-way and higher data
   • chromatography with multi-channel detector (e.g. GC-MS)
   • fluorescence excitation-emission matrices (EEM)
   • batch process, etc.
   Handling 3-way data in MATLAB
2. Multi-way Models
   PARAFAC
   PARAFAC2
   Tucker
3. Regression modeling with Multi-way data
   Unfolding
   Multi-linear PLS