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arXiv · 2509.19474

Quantum Harmonic Analysis and the Structure in Data: Augmentation

Monika Doerfler·Franz Luef·Henry McNulty
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Abstract

In this short note, we study the impact of data augmentation on the smoothness of principal components of high-dimensional datasets. Using tools from quantum harmonic analysis, we show that eigenfunctions of operators corresponding to augmented data sets lie in the modulation space $M^1(\mathbb{R}^d)$, guaranteeing smoothness and continuity. Numerical examples on synthetic and audio data confirm the theoretical findings. While interesting in itself, the results suggest that manifold learning and feature extraction algorithms can benefit from systematic and informed augmentation principles.

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Monika Doerfler, Franz Luef, Henry McNulty. 2025-09-23. Quantum Harmonic Analysis and the Structure in Data: Augmentation. https://arxiv.org/abs/2509.19474

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