TY - JOUR T1 - Theoretical bounds on data requirements for the ray-based classification JF - SN Comput. Sci. Y1 - 2022 A1 - Brian J. Weber A1 - Sandesh S. Kalantre A1 - Thomas McJunkin A1 - J. M. Taylor A1 - Justyna P. Zwolak AB -

The problem of classifying high-dimensional shapes in real-world data grows in complexity as the dimension of the space increases. For the case of identifying convex shapes of different geometries, a new classification framework has recently been proposed in which the intersections of a set of one-dimensional representations, called rays, with the boundaries of the shape are used to identify the specific geometry. This ray-based classification (RBC) has been empirically verified using a synthetic dataset of two- and three-dimensional shapes [1] and, more recently, has also been validated experimentally [2]. Here, we establish a bound on the number of rays necessary for shape classification, defined by key angular metrics, for arbitrary convex shapes. For two dimensions, we derive a lower bound on the number of rays in terms of the shape's length, diameter, and exterior angles. For convex polytopes in R^N, we generalize this result to a similar bound given as a function of the dihedral angle and the geometrical parameters of polygonal faces. This result enables a different approach for estimating high-dimensional shapes using substantially fewer data elements than volumetric or surface-based approaches.

VL - 3 UR - https://arxiv.org/abs/2103.09577 CP - 57 U5 - https://doi.org/10.1007/s42979-021-00921-0 ER -