Signal-Plus-Noise Decomposition of Nonlinear Spiked Random Matrix Models

Fuente: arXiv
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Auteurs principaux: Moniri, Behrad, Hassani, Hamed
Format: Preprint
Publié: 2024
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author Moniri, Behrad
Hassani, Hamed
author_facet Moniri, Behrad
Hassani, Hamed
contents In this paper, we study a nonlinear spiked random matrix model where a nonlinear function is applied element-wise to a noise matrix perturbed by a rank-one signal. We establish a signal-plus-noise decomposition for this model and identify precise phase transitions in the structure of the signal components at critical thresholds of signal strength. To demonstrate the applicability of this decomposition, we then utilize it to study new phenomena in the problems of signed signal recovery in nonlinear models and community detection in transformed stochastic block models. Finally, we validate our results through a series of numerical simulations.
format Preprint
id arxiv_https___arxiv_org_abs_2405_18274
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Signal-Plus-Noise Decomposition of Nonlinear Spiked Random Matrix Models
Moniri, Behrad
Hassani, Hamed
Statistics Theory
Machine Learning
Signal Processing
In this paper, we study a nonlinear spiked random matrix model where a nonlinear function is applied element-wise to a noise matrix perturbed by a rank-one signal. We establish a signal-plus-noise decomposition for this model and identify precise phase transitions in the structure of the signal components at critical thresholds of signal strength. To demonstrate the applicability of this decomposition, we then utilize it to study new phenomena in the problems of signed signal recovery in nonlinear models and community detection in transformed stochastic block models. Finally, we validate our results through a series of numerical simulations.
title Signal-Plus-Noise Decomposition of Nonlinear Spiked Random Matrix Models
topic Statistics Theory
Machine Learning
Signal Processing
url https://arxiv.org/abs/2405.18274