Name
Abstract | ||
|---|---|---|
In this paper we propose a concavely regularized convex relaxation based graph matching algorithm. The graph matching problem is firstly formulated as a constrained convex quadratic program by relaxing the feasible set from the permutation matrices to doubly stochastic matrices. To gradually push the doubly stochastic matrix back to be a permutation one, an objective function is constructed by adding a simple weighted concave regularization to the convex relaxation. By gradually increasing the weight of the concave term, minimization of the objective function will gradually push the doubly stochastic matrix back to be a permutation one. A concave-convex procedure (CCCP) together with the Frank-Wolfe algorithm is adopted to minimize the objective function. The algorithm can be used on any types of graphs and exhibits a comparable performance as the PATH following algorithm, a state-of-the-art graph matching algorithm but applicable only on undirected graphs. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.neucom.2012.12.065 | Neurocomputing |
Keywords | Field | DocType |
frank-wolfe algorithm,convex quadratic program,path following algorithm,concavely regularized convex relaxation,permutation matrix,state-of-the-art graph,stochastic matrix,undirected graph,objective function,convex relaxation,graph matching,frank wolfe algorithm | Combinatorics,Doubly stochastic matrix,Effective domain,Algorithm,Permutation matrix,Matching (graph theory),Frank–Wolfe algorithm,Factor-critical graph,Proper convex function,Mathematics,Convex analysis | Journal |
Volume | ISSN | Citations |
134, | 0925-2312 | 0 |
PageRank | References | Authors |
0.34 | 20 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiyong Liu | 1 | 659 | 75.59 |
| Hong Qiao | 2 | 1147 | 110.95 |
| Li-Hao Jia | 3 | 3 | 1.40 |
| Lei Xu | 4 | 3590 | 387.32 |