Bispectral OT: Dataset Comparison using Symmetry-Aware Optimal Transport

• URL: http://arxiv.org/abs/2509.20678v1
• Date: Thu, 25 Sep 2025 02:25:24 GMT
• Title: Bispectral OT: Dataset Comparison using Symmetry-Aware Optimal Transport
• Authors: Annabel Ma, Kaiying Hou, David Alvarez-Melis, Melanie Weber,
• Abstract summary: Bispectral Optimal Transport is a symmetry-aware extension of discrete OT that compares elements using their representation using the bispectrum.<n>We demonstrate that the transport plans computed with Bispectral OT achieve greater class preservation accuracy than naive feature OT on benchmark datasets transformed with visual symmetries.
• Score: 10.296285376520244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optimal transport (OT) is a widely used technique in machine learning, graphics, and vision that aligns two distributions or datasets using their relative geometry. In symmetry-rich settings, however, OT alignments based solely on pairwise geometric distances between raw features can ignore the intrinsic coherence structure of the data. We introduce Bispectral Optimal Transport, a symmetry-aware extension of discrete OT that compares elements using their representation using the bispectrum, a group Fourier invariant that preserves all signal structure while removing only the variation due to group actions. Empirically, we demonstrate that the transport plans computed with Bispectral OT achieve greater class preservation accuracy than naive feature OT on benchmark datasets transformed with visual symmetries, improving the quality of meaningful correspondences that capture the underlying semantic label structure in the dataset while removing nuisance variation not affecting class or content.

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