Robust laplacian matrix learning for smooth graph signals

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - International Conference on Image Processing, ICIP
PublisherIEEE Computer Society
Pages1878-1882
Volume2016-August
ISBN (Print)9781467399616
Publication statusPublished - 3 Aug 2016
Externally publishedYes

Publication series

Name
Volume2016-August
ISSN (Print)1522-4880

Conference

Title23rd IEEE International Conference on Image Processing (ICIP 2016)
LocationPhoenix Convention Center
PlaceUnited States
CityPhoenix
Period25 - 28 September 2016

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%.

Research Area(s)

  • Graph signal processing, Laplacian matrix, Robustness

Citation Format(s)

Robust laplacian matrix learning for smooth graph signals. / Hou, Junhui; Chau, Lap-Pui; He, Ying; Zeng, Huanqiang.

Proceedings - International Conference on Image Processing, ICIP. Vol. 2016-August IEEE Computer Society, 2016. p. 1878-1882 7532684.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review