Pseudo-Non-Linear Data Augmentation via Energy Minimization

Pingbang Hu, Mahito Sugiyama

Keywords: data augmentation, information geometry, interpretability

TL;DR: We provide an interpretable data augmentation method via information geometry with a data-centric algorithm.

Abstract:

We propose a novel and interpretable data augmentation method based on energy-based modeling and principles from information geometry. Unlike black-box generative models, which rely on deep neural networks, our approach replaces these non-interpretable transformations with explicit, theoretically grounded ones, ensuring interpretability and strong guarantees such as energy minimization. Central to our method is the introduction of the backward projection algorithm, which reverses dimension reduction to generate new data. Empirical results demonstrate that our method achieves competitive performance with black-box generative models while offering greater transparency and interpretability.

Primary Area: learning on graphs and other geometries & topologies

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Submission Number: 6172