This work approaches the multidimensional scaling problem from a novel angle. We introduce a scalable method based on the h-plot, which inherently accommodates asymmetric proximity data. Instead of embedding the objects themselves, the method embeds the variables that define the proximity to or from each object. It is straightforward to implement, and the quality of the resulting representation can be easily evaluated. The methodology is illustrated by visualizing the asymmetric relationships between the citing and cited profiles of journals on a common map. Two profiles that are far apart (or close together) in the h-plot, as measured by Euclidean distance, are different (or similar), respectively. This representation allows archetypoid analysis (ADA) to be calculated. ADA is used to find archetypal journals (or extreme cases). We can represent the dataset as convex combinations of these archetypal journals, making the results easy to interpret, even for non-experts. Comparisons with other methodologies are carried out, showing the good performance of our proposal. Code and data are available for reproducibility.

翻译：本研究从一个新颖的视角处理多维尺度分析问题。我们引入了一种基于h-plot的可扩展方法，该方法本质上适用于非对称邻近度数据。该方法并非直接嵌入对象本身，而是嵌入定义每个对象邻近关系的变量。该方法实现简便，且所得表征的质量易于评估。我们通过将期刊的引用与被引用特征之间的非对称关系可视化于同一图谱中，对该方法进行了演示。在h-plot中，以欧氏距离度量的两个特征相距较远（或较近），分别表示它们不同（或相似）。这种表征方式使得原型分析（ADA）得以计算。ADA用于发现原型期刊（或极端案例）。我们可以将数据集表示为这些原型期刊的凸组合，从而使结果易于解释，即使对非专业人士而言也是如此。通过与其他方法的比较，展示了本方案的良好性能。代码和数据已公开以确保可复现性。