3 papers
cs.LG2026
Group Invariant Spectral Embedding
Yeari Vigder, Paulina Hoyos, David Thong +3
Spectral embedding methods are widely used for dimensionality reduction and clustering of high-dimensional datasets with intrinsic low-dimensional structures. Although many dataset…
cs.LG2026
Exact Symmetry as Algebra: A Machine-Verified Tensor Calculus that Enforces Physical Selection Rules
Paulina Hoyos, Shashanka Ubaru, Dongsung Huh +5
Symmetry is central to the physical sciences, yet machine learning usually captures it only approximately, leaving a residual per-step equivariance error that compoun…
eess.SP2025
SO(3)-invariant PCA with application to molecular data
Michael Fraiman, Paulina Hoyos, Tamir Bendory +4
Principal component analysis (PCA) is a fundamental technique for dimensionality reduction and denoising; however, its application to three-dimensional data with arbitrary orientat…