Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Journal articles
  4. Low-rank matrix estimation with inhomogeneous noise
 
research article

Low-rank matrix estimation with inhomogeneous noise

Guionnet, Alice
•
Ko, Justin
•
Krzakala, Florent  
Show more
June 1, 2025
Information and Inference

We study low-rank matrix estimation for a generic inhomogeneous output channel through which the matrix is observed. This generalizes the commonly considered spiked matrix model with homogeneous noise to include for instance the dense degree-corrected stochastic block model. We adapt techniques used to study multi-species spin glasses to derive and rigorously prove an expression for the free energy of the problem in the large size limit, providing a framework to study the signal detection thresholds. We discuss an application of this framework to the degree corrected stochastic block models.

  • Details
  • Metrics
Type
research article
DOI
10.1093/imaiai/iaaf010
Scopus ID

2-s2.0-105002830677

Author(s)
Guionnet, Alice

Unité de Mathématiques Pures et Appliquées de l'ENS de Lyon

Ko, Justin

Unité de Mathématiques Pures et Appliquées de l'ENS de Lyon

Krzakala, Florent  

École Polytechnique Fédérale de Lausanne

Zdeborová, Lenka  

École Polytechnique Fédérale de Lausanne

Date Issued

2025-06-01

Published in
Information and Inference
Volume

14

Issue

2

Article Number

iaaf010

Subjects

low rank matrix estimation

•

minimal mean squared error

•

mutual information

•

spin glasses

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
IDEPHICS1  
SPOC1  
FunderFunding(s)Grant NumberGrant URL

European Research Council

European Union Horizon 2020 research and innovation pro-gramme

European Union Horizon 2020 research and innovation programme

884584

Show more
Available on Infoscience
April 29, 2025
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/249469
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés