Schaeffer, Joachim ; Braatz, Richard D. (2022)
Latent Variable Method Demonstrator - software for understanding multivariate data analytics algorithms.
In: Computers and Chemical Engineering, 167
doi: 10.1016/j.compchemeng.2022.108014
Artikel, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Schaeffer, Joachim ; Braatz, Richard D. |
| Art des Eintrags: | Bibliographie |
| Titel: | Latent Variable Method Demonstrator - software for understanding multivariate data analytics algorithms |
| Sprache: | Englisch |
| Publikationsjahr: | 1 November 2022 |
| Verlag: | Elsevier |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Computers and Chemical Engineering |
| Jahrgang/Volume einer Zeitschrift: | 167 |
| DOI: | 10.1016/j.compchemeng.2022.108014 |
| Zugehörige Links: | |
| Kurzbeschreibung (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. |
| ID-Nummer: | 108014 |
| Zusätzliche Informationen: | Art.No.: 108014 |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik > Control and Cyber-Physical Systems (CCPS) |
| Hinterlegungsdatum: | 19 Okt 2022 12:03 |
| Letzte Änderung: | 19 Okt 2022 12:03 |
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