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
Author(s)
Detail(s)
Original language | English |
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Title of host publication | Proceedings - International Conference on Image Processing, ICIP |
Publisher | IEEE Computer Society |
Pages | 1878-1882 |
Volume | 2016-August |
ISBN (Print) | 9781467399616 |
Publication status | Published - 3 Aug 2016 |
Externally published | Yes |
Publication series
Name | |
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Volume | 2016-August |
ISSN (Print) | 1522-4880 |
Conference
Title | 23rd IEEE International Conference on Image Processing (ICIP 2016) |
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Location | Phoenix Convention Center |
Place | United States |
City | Phoenix |
Period | 25 - 28 September 2016 |
Link(s)
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 et al.
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