论文标题
潜在变量方法演示器 - 用于理解多元数据分析算法的软件
Latent Variable Method Demonstrator -- Software for Understanding Multivariate Data Analytics Algorithms
论文作者
论文摘要
越来越多的多元过程数据的数量正在推动熟练的工程师从这些数据分析，解释和构建模型的需求。多元数据分析在很大程度上依赖于线性代数，优化和统计数据，并且鉴于大多数课程在后三个主题中没有强大的覆盖范围，学生可能会具有挑战性。本文介绍了用于教学，学习和理解潜在变量方法的交互式软件 - 潜在变量演示器（LAVADE）。在此软件中，用户可以与其他回归方法（例如绝对绝对收缩和选择操作员（LASSO），Ridge Remission（RR）（RR）和Elastic Net（EN）（EN）（EN）（EN）（EN），用户可以交互性地比较潜在变量方法，例如部分最小二乘（PLS）和主组件回归（PCR）。 Lavade有助于建立有关选择适当的方法，超参数调整和模型系数解释的直觉，从而促进对算法差异的概念理解。该软件包含数据生成方法和三个化学过程数据集，可以比较具有不同级别复杂性的数据集的结果。 Lavade作为开源软件发布，以便其他人可以应用并推进用于教学或研究的工具。
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.
