Locally Permuted Low Rank Column-wise Sensing
Ahmed Ali Abbasi, Namrata Vaswani
公開日: 2025/9/11
Abstract
We precisely formulate, and provide a solution for, the Low Rank Columnwise Sensing (LRCS) problem when some of the observed data is scrambled/permuted/unlabeled. This problem, which we refer to as permuted LRCS, lies at the intersection of two distinct topics of recent research: unlabeled sensing and low rank column-wise (matrix) sensing. We introduce a novel generalization of the recently developed Alternating Gradient Descent and Minimization (AltGDMin) algorithm to solve this problem. We also develop an alternating minimization (AltMin) solution. We show, using simulation experiments, that both converge but PermutedAltGDmin is much faster than Permuted-AltMin.