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2018 24th International Conference on Pattern Recognition (ICPR) (2018)
Beijing, China
Aug. 20, 2018 to Aug. 24, 2018
ISSN: 1051-4651
ISBN: 978-1-5386-3789-0
pp: 1366-1371
M. Curado , Department of Computer Science and AI, University of Alicante, Alicante, 03690, Spain
F. Escolano , Department of Computer Science and AI, University of Alicante, Alicante, 03690, Spain
M.A. Lozano , Department of Computer Science and AI, University of Alicante, Alicante, 03690, Spain
E.R. Hancock , Department of Computer Science, University of York York, YO10 5DD, UK
ABSTRACT
In this paper, we propose net4Lap, a novel architecture for Laplacian-based ranking. The two main ingredients of the approach are: a) pre-processing graphs with neural embed-dings before performing Laplacian ranking, and b) introducing a global measure of centrality to modulate the diffusion process. We explicitly formulate ranking as an optimization problem where regularization is emphasized. This formulation is a theoretical tool to validate our approach. Finally, our experiments show that the proposed architecture significantly outperforms state-of-the-art rankers and it is also a proper tool for re-ranking.
INDEX TERMS
Manifolds, Laplace equations, Harmonic analysis, Absorption, Optimization, Correlation, Minimization
CITATION

M. Curado, F. Escolano, M. Lozano and E. Hancock, "net4Lap: Neural Laplacian Regularization for Ranking and Re-Ranking," 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China, 2018, pp. 1366-1371.
doi:10.1109/ICPR.2018.8545303
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