An Eigendecomposition Approach to Weighted Graph Matching Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature-based correspondence: an eigenvector approach
Image and Vision Computing - Special issue: BMVC 1991
SIAM Review
A Spectral Algorithm for Seriation and the Consecutive Ones Problem
SIAM Journal on Computing
Semidefinite programming relaxations for the graph partitioning problem
Discrete Applied Mathematics - Special issue on the satisfiability problem and Boolean functions
Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Edit Distance from Spectral Seriation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semidefinite Programming Heuristics for Surface Reconstruction Ambiguities
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
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Graph seriation is concerned with placing the nodes of a graph in a serial order so that edge consecutive constraints are generally preserved. It is an important task in network analysis problem in routine and bioinformatics. In this paper we show how the problem of graph seriation can be solved using semi-definite programming (SDP). This is a convex optimisation procedure that has recently found widespread use in computer vision. The main contribution of the paper is to detail the matrix representation needed to cast the graph-seriation problem in a matrix setting so that it can be solved using SDP. We illustrate the utility of the method for graph-matching and graph-clustering, where it is shown to offer advantages to the graph-spectral approach to seriation.