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