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Pattern Recognition, International Conference on (2004)
Cambridge UK
Aug. 23, 2004 to Aug. 26, 2004
ISSN: 1051-4651
ISBN: 0-7695-2128-2
pp: 402-405
Bin Luo , University of York, UK
Richard C. Wilson , University of York, UK
Edwin R. Hancock , University of York, UK
ABSTRACT
Gaph structures have proved computationally cumbersome for pattern analysis. The reason for this is that before graphs can be converted to pattern vectors, correspondences must be established between the nodes of structures which are potentially of different size. To overcome this problem, in this paper we turn to the spectral decomposition of the Laplacian matrix. We show how the elements of the spectral matrix for the Laplacian can be used to construct symmetric polynomials that are permutation invariants. The co-efficients of these polynomials can be used as graph-features which can be encoded in a vectorial manner. We explore whether the vectors of invariants canbe embedded in a low dimensional space using a number of alternative strategies including principal components analysis (PCA), multidimensional scaling (MDS) and locality preserving projection (LPP).
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CITATION

R. C. Wilson, E. R. Hancock and B. Luo, "Graph Manifolds from Spectral Polynomials," Pattern Recognition, International Conference on(ICPR), Cambridge UK, 2004, pp. 402-405.
doi:10.1109/ICPR.2004.1334551
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