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A spectral approach to learning structural variations in graphs

Authors
Journal
Pattern Recognition
0031-3203
Publisher
Elsevier
Publication Date
Volume
39
Issue
6
Identifiers
DOI: 10.1016/j.patcog.2006.01.001
Keywords
  • Generative Model
  • Graph
  • Covariance Matrix
  • Clustering

Abstract

Abstract 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.

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