Latent Variable Method Demonstrator -- Software for Understanding Multivariate Data Analytics Algorithms
This provides a tool for students and engineers to better understand complex data analytics algorithms, but it is incremental as it focuses on educational software rather than new algorithmic advances.
The authors tackled the challenge of teaching latent variable methods for multivariate data analytics by developing LAVADE, an interactive open-source software that helps users compare methods like PLS and PCR with regression techniques, fostering conceptual understanding through data generation and real datasets.
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.