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- W201990040 abstract "In this paper we propose a regularized relaxation based graph matching algorithm. The graph matching problem is formulated as a constrained convex quadratic program, by relaxing the permutation matrix to a doubly stochastic one. To gradually push the doubly stochastic matrix back to a permutation one, a simple weighted concave regular term is added to the convex objective function. The concave regular function is not a concave relaxation of the original matching problem. However, it is shown that such a simple concave regular term has a comparative performance as the concave relaxation of the PATH following algorithm, which works only on undirected graphs. A concave-convex procedure (CCCP) together with the Frank-Wolfe algorithm is adopted to solve the matching problem, and some experimental results witness the state-of-art performance of the proposed algorithm." @default.
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- W201990040 date "2012-01-01" @default.
- W201990040 modified "2023-09-23" @default.
- W201990040 title "A Weight Regularized Relaxation Based Graph Matching Algorithm" @default.
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- W201990040 doi "https://doi.org/10.1007/978-3-642-31919-8_2" @default.
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