Shape Proportions and Sphericity in n Dimensions
This work addresses the need for qualitative shape understanding in high-dimensional data analysis, though it appears incremental as it extends existing low-dimensional metrics to any dimension.
The paper tackles the problem of lacking single-number shape metrics for objects in high dimensions by introducing two new metrics, hyper-Sphericity and hyper-Shape Proportion, and demonstrates their application on shapes like n-balls and datasets such as Iris.
Shape metrics for objects in high dimensions remain sparse. Those that do exist, such as hyper-volume, remain limited to objects that are better understood such as Platonic solids and $n$-Cubes. Further, understanding objects of ill-defined shapes in higher dimensions is ambiguous at best. Past work does not provide a single number to give a qualitative understanding of an object. For example, the eigenvalues from principal component analysis results in $n$ metrics to describe the shape of an object. Therefore, we need a single number which can discriminate objects with different shape from one another. Previous work has developed shape metrics for specific dimensions such as two or three dimensions. However, there is an opportunity to develop metrics for any desired dimension. To that end, we present two new shape metrics for objects in a given number of dimensions: hyper-Sphericity and hyper-Shape Proportion (SP). We explore the proprieties of these metrics on a number of different shapes including $n$-balls. We then connect these metrics to applications of analyzing the shape of multidimensional data such as the popular Iris dataset.