Maximal level estimation and unbalance reduction for graph signal downsampling
Zheng X.1; Tang Y.Y.1; Zhou J.1; Wang P.2
Conference Name23rd International Conference on Pattern Recognition (ICPR)
Source PublicationProceedings - International Conference on Pattern Recognition
Conference DateDEC 04-08, 2016
Conference PlaceMexican Assoc Comp Vis Robot & Neural Comp, Cancun, MEXICO

The emerging field of graph signal processing requires a solid design of downsampling operation for graph signals to extend pattern recognition, machine learning and signal processing techniques into the graph setting. The state-of-the-art downsampling method is constructed upon the maximum spanning trees of the graphs. However, under the framework of this method, unbalanced downsampling often occurs for signals defined on densely connected unweighted graphs, such as social network data. The unbalance also significantly reduces the maximal downsampling level, making it smaller than the level we expect. In applications, the maximal level must be estimated to ensure that it is larger than the expected level; meanwhile, the unbalance has to be reduced, if it occurs. In this paper, we propose a novel method to jointly estimate the maximal level and reduce the downsampling unbalance. This method also offers an estimation of the possibility of unbalanced downsampling. If a graph signal is classified to be with high unbalance possibility, the maximum spanning tree will be updated to generate a balanced downsampling. The simulation results on synthesis and real world data support the theoretical analysis.

URLView the original
Indexed BySCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000406771303151
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
Affiliation1.Universidade de Macau
2.Northeastern University
Recommended Citation
GB/T 7714
Zheng X.,Tang Y.Y.,Zhou J.,et al. Maximal level estimation and unbalance reduction for graph signal downsampling[C],2017:3922-3926.
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