In this paper we analyze the use of regularization kernels on graphs to weight the quadratic cost function used in the Softassign graph-matching algorithm.
Abstract. In this paper we analyze the use of regularization kernels on graphs to weight the quadratic cost function used in the Softassign.
In this paper we analyze the use of regularization kernels on graphs to weight the quadratic cost function used in the Softassign graph-matching algorithm.
Regularization Kernels and Softassign. M. Lozano, and F. Escolano. CIARP, volume 3287 of Lecture Notes in Computer Science, page 320-327. Springer, (2004 ).
Kernelization of Softassign and Motzkin-Strauss Algorithms
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This paper reviews two continuous methods for graph matching: Softassign and Replicator Dynamics. These methods can be applied to non-attributed graphs, ...
Jun 28, 2018 · There are many ways that we might choose to regularize, but we will focus on the common Tikhonov regularization approach. As always in kernel ...
In this paper we address the problem of comparing and classifying protein surfaces through a kernelized version of the Softassign graph-matching algorithm.
This paper reviews two continuous methods for graph matching: Softassign and Replicator Dynamics. These methods can be applied to non-attributed graphs, ...
In this paper we address the problem of graph matching and graph classification through a kernelized version of the classical Softassign method.
After regu- larization, our matching subroutine resembles “softassign quadratic assignment” (Rangarajan et al., 1999; Gold &. Rangarajan, 1996). We extend their ...