Group-Aware Matrix Estimation and Latent Subspace Recovery

ArXi:2605.20559v1 Announce Type: cross Modern matrix completion problems often involve heterogeneous data whose rows simultaneously belong to many meta-categories, such as graphic and age groups in recommendation systems, or region and recording session labels in neural electrophysiological experiments. Standard low-rank estimators impose a single global latent geometry, which can recover average structure but may smooth away subgroup-specific variation, especially when observations are unevenly distributed across groups. We.