Techniques to visualise and understand large amounts of data are of paramount importance. In most settings, this data is usually multivariate, which further stresses the need for effective visualisation techniques. Multivariate visualisation techniques such as canonical variate analysis (CVA) biplots allow for simultaneous lower-dimensional visualisation and data classification by incorporating class-specific data. CVA biplots, however, are currently restricted to numerical data. Through combining concepts from both CVA and non-linear principal component analysis (PCA) biplots, a new biplot construction methodology that improves on the traditional CVA biplot by allowing for categorical variables is proposed. This technique, named CVA(H_r), is showcased using the established mushroom data set, which contains a mix of categorical and ordinal variables. This novel method improves upon existing biplot construction in terms of classification accuracy and class separation.

可视化和理解大量数据的技术至关重要。在大多数情况下，这些数据通常是多变量的，这进一步强调了对有效可视化技术的需求。多变量可视化技术，例如规范变量分析 (CVA) 双标图，允许通过合并特定于类的数据同时进行低维可视化和数据分类。然而，CVA 双标图目前仅限于数值数据。通过结合 CVA 和非线性主成分分析 (PCA) 双标图的概念，提出了一种新的双标图构建方法，该方法通过允许分类变量来改进传统的 CVA 双标图。这种技术，命名为 CVA(H _r)，使用既定的蘑菇数据集进行展示，该数据集包含分类变量和有序变量的混合。这种新方法在分类精度和类分离方面改进了现有的双标图构造。