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Proceedings Paper

Eigenspaces for graphs from spectral features
Author(s): Bin Luo; Richard C. Wilson; Edwin R. Hancock
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Paper Abstract

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.

Paper Details

Date Published: 31 July 2002
PDF: 8 pages
Proc. SPIE 4875, Second International Conference on Image and Graphics, (31 July 2002); doi: 10.1117/12.477068
Show Author Affiliations
Bin Luo, Anhui Univ. and Univ. of York (United Kingdom)
Richard C. Wilson, Univ. of York (United Kingdom)
Edwin R. Hancock, Univ. of York (United Kingdom)

Published in SPIE Proceedings Vol. 4875:
Second International Conference on Image and Graphics
Wei Sui, Editor(s)

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