Locally Permuted Low Rank Column-wise Sensing

Fuente: arXiv
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Auteurs principaux: Abbasi, Ahmed Ali, Vaswani, Namrata
Format: Preprint
Publié: 2025
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author Abbasi, Ahmed Ali
Vaswani, Namrata
author_facet Abbasi, Ahmed Ali
Vaswani, Namrata
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2509_09820
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Locally Permuted Low Rank Column-wise Sensing
Abbasi, Ahmed Ali
Vaswani, Namrata
Signal Processing
Information Theory
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.
title Locally Permuted Low Rank Column-wise Sensing
topic Signal Processing
Information Theory
url https://arxiv.org/abs/2509.09820