Function-Counting Theory for Low-Dimensional Data Structures

Konstantin Häberle; Helmut Bölcskei

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arXiv · 2607.01010

Function-Counting Theory for Low-Dimensional Data Structures

Konstantin Häberle·Helmut Bölcskei

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Abstract

The success of deep learning models in classification and regression is widely attributed to the low-dimensional structure that real-world data tend to exhibit, despite their high-dimensional representation. This work attempts to provide a mathematical framework for binary classification on low-dimensional data, building on Cover's (1965) function-counting theory. With our framework, we aim to address the question of how the low-dimensional structure of the data affects the classification capabilities of learning models. Cover's theory relies on a general position assumption that blinds it to the underlying data structure. We refine this assumption to account for the low-dimensionality of the data and derive dichotomy counts that reflect the data structure. We further extend Cover's separation capacity and problem of generalization to the low-dimensional setting, enabling the impact of the underlying data structure on both to be analyzed.

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BibTeX RIS

Konstantin Häberle, Helmut Bölcskei. 2026-07-01. Function-Counting Theory for Low-Dimensional Data Structures. https://arxiv.org/abs/2607.01010

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