Efficient Robust Graph Learning Based on Minimax Concave Penalty and ?-Cross Entropy

Tatsuya Koyakumaru, Masahiro Yukawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents an efficient robust method to learn sparse graphs from contaminated data. Specifically, the convex-analytic approach using the minimax concave penalty is formulated using the so-called ?-lasso which exploits the ?-cross entropy. We devise a weighting technique which designs the data weights based on the l1 distance in addition to the Mahalanobis distance for avoiding possible failures of outlier rejection due to the combinatorial graph Laplacian structure. Numerical examples show that the proposed method significantly outperforms ?-lasso and t-lasso as well as the existing non-robust graph learning methods in contaminated situations.

Original languageEnglish
Title of host publication30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages1776-1780
Number of pages5
ISBN (Electronic)9789082797091
Publication statusPublished - 2022
Event30th European Signal Processing Conference, EUSIPCO 2022 - Belgrade, Serbia
Duration: 2022 Aug 292022 Sep 2

Publication series

NameEuropean Signal Processing Conference
Volume2022-August
ISSN (Print)2219-5491

Conference

Conference30th European Signal Processing Conference, EUSIPCO 2022
Country/TerritorySerbia
CityBelgrade
Period22/8/2922/9/2

Keywords

  • graph learning
  • minimax concave penalty
  • robust statistics
  • γ-cross entropy

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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