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Published Articles >> Table of Contents >> Abstract
18th International Conference on Pattern Recognition (ICPR'06) Volume 4
pp. 140-144
Mixing spectral representations of graphs
David White, University of York, UK
Richard C. Wilson, University of York, UK
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.803
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| Abstract |
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Generative models are well known in the domain of statistical
pattern recognition. Typically, they describe the
probability distribution of patterns in a vector space. The
individual patterns are defined by vectors and so the individual
features of the pattern are well defined. In contrast,
very little has been done with generative models of graphs.
Graphs are not naturally represented in a vector space since
there is no natural labelling of the vertices of the graphs -
different labellings lead to different representations of the
graph structure. Because of this, simple statistical quantities
such as mean and variance are difficult to define for a
group of graphs. While we can define statistical quantities
of individual edges, it is not so straightforward to define
how sets of edges in graphs are related. The spectral decomposition
of a graph can be used to extract information
about the relationship of edges and parts in a graph. In
this paper we look at the problem of mixing graphs by using
the spectral representation of a graph as an intermediate
step. The spectral representation allows us to mix different
structural features from each of the graphs to create new
combinations. We can also define an averaging process on
the spectral representations which generates a graph close
to the graph median.
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 Additional Information
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Citation:
David White, Richard C. Wilson,
"Mixing spectral representations of graphs,"
icpr,
pp. 140-144,
18th International Conference on Pattern Recognition (ICPR'06) Volume 4,
2006
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