Intrinsic Dimension of Geometric Data Sets
This work addresses the curse of dimensionality for researchers in machine learning and knowledge discovery by providing a novel geometric framework, though it appears incremental as it builds on existing mathematical theories.
The authors tackled the problem of the curse of dimensionality in machine learning by developing a comprehensive geometric model for data sets based on Gromov's and Pestov's theories, resulting in a computationally feasible and adaptable intrinsic dimension function validated through experiments.
The curse of dimensionality is a phenomenon frequently observed in machine learning (ML) and knowledge discovery (KD). There is a large body of literature investigating its origin and impact, using methods from mathematics as well as from computer science. Among the mathematical insights into data dimensionality, there is an intimate link between the dimension curse and the phenomenon of measure concentration, which makes the former accessible to methods of geometric analysis. The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension. In detail, we define a concept of geometric data set and introduce a metric as well as a partial order on the set of isomorphism classes of such data sets. Based on these objects, we propose and investigate an axiomatic approach to the intrinsic dimension of geometric data sets and establish a concrete dimension function with the desired properties. Our model for data sets and their intrinsic dimension is computationally feasible and, moreover, adaptable to specific ML/KD-algorithms, as illustrated by various experiments.