Robust laplacian matrix learning for smooth graph signals

Junhui Hou, Lap Pui Chau, Ying He, Huanqiang Zeng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)

Abstract

We propose a new method for robust learning Laplacian matrices from observed smooth graph signals in the presence of both Gaussian noise and random-valued impulse noise (i.e., outliers). Using the recently developed factor analysis model for representing smooth graph signals in [1], we formulate our learning process as a constrained optimization problem, and adopt the £i-norm for measuring the data fidelity in order to improve robustness. Computational results on three types of synthetic graphs demonstrate that the proposed method outperforms the state-of-the-art methods in terms of commonly used information retrieval metrics, such as F-measure, precision, recall and normalized mutual information. In particular, we observed that F-measure is improved by up to 16%.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PublisherIEEE Computer Society
Pages1878-1882
Number of pages5
ISBN (Electronic)9781467399616
DOIs
Publication statusPublished - 3 Aug 2016
Externally publishedYes
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
Duration: 25 Sep 201628 Sep 2016

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2016-August
ISSN (Print)1522-4880

Conference

Conference23rd IEEE International Conference on Image Processing, ICIP 2016
Country/TerritoryUnited States
CityPhoenix
Period25/09/1628/09/16

Keywords

  • Graph signal processing
  • Laplacian matrix
  • Robustness

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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