Latent Variable Method Demonstrator -- Software for Understanding Multivariate Data Analytics Algorithms

TL;DR

LAVADE is an interactive open-source software tool designed to help students and researchers understand and compare multivariate data analysis methods like PLS, PCR, and regularization techniques through visualization and data simulation.

Contribution

The paper introduces LAVADE, a novel interactive software platform that enhances understanding of latent variable methods and regression techniques in multivariate data analysis education.

Findings

Facilitates comparison of different multivariate methods

Supports hyperparameter tuning and interpretation

Includes datasets for diverse complexity levels

Abstract

The ever-increasing quantity of multivariate process data is driving a need for skilled engineers to analyze, interpret, and build models from such data. Multivariate data analytics relies heavily on linear algebra, optimization, and statistics and can be challenging for students to understand given that most curricula do not have strong coverage in the latter three topics. This article describes interactive software - the Latent Variable Demonstrator (LAVADE) - for teaching, learning, and understanding latent variable methods. In this software, users can interactively compare latent variable methods such as Partial Least Squares (PLS), and Principal Component Regression (PCR) with other regression methods such as Least Absolute Shrinkage and Selection Operator (lasso), Ridge Regression (RR), and Elastic Net (EN). LAVADE helps to build intuition on choosing appropriate methods, hyperparameter tuning, and model coefficient interpretation, fostering a conceptual understanding of the algorithms' differences. The software contains a data generation method and three chemical process datasets, allowing for comparing results of datasets with different levels of complexity. LAVADE is released as open-source software so that others can apply and advance the tool for use in teaching or research.