A Spectral Approach to Learning Structural Variations in Graphs

This paper shows how to construct a linear deformable model for graph structure by performing principal components analysis (PCA) on the vectorised adjacency matrix. We commence by using correspondence information to place the nodes of each of a set of graphs in a standard reference order. Using the correspondences order, we convert the adjacency matrices to long-vectors and compute the long-vector covariance matrix. By projecting the vectorised adjacency matrices onto the leading eigenvectors of the covariance matrix, we embed the graphs in a pattern-space. We illustrate the utility of the resulting method for shape-analysis. ᭧ 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

• Type: Conference
• Year: 2003
• Where: ICVS
• Authors: Bin Luo, Richard C. Wilson, Edwin R. Hancock
• Keywords: Adjacency Matrices, Covariance Matrix, ICVS 2003, Vectorised Adjacency