Eigenspaces for graphs from spectral features
Bin Luo, Richard C. Wilson, Edwin R. Hancock · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2002
In this paper we explore how to embed symbolic relational graphs with unweighted edges in eigenspaces. We adopt a graph-spectral approach. The leading eigenvectors of the graph adjacency matrix are used to define clusters of nodes. For each cluster, we compute vectors of spectral properties. We embed these vectors in a pattern-space using principal components analysis and multidimensional scaling techniques. We demonstrate both methods result in well-structured view spaces for graph-data extracted from 2D views of 3D objects.