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On the sample mean of graphs | IEEE Conference Publication | IEEE Xplore

On the sample mean of graphs


Abstract:

We present an analytic and geometric view of the sample mean of graphs. The theoretical framework yields efficient subgradient methods for approximating a structural mean...Show More

Abstract:

We present an analytic and geometric view of the sample mean of graphs. The theoretical framework yields efficient subgradient methods for approximating a structural mean and a simple plug-in mechanism to extend existing central clustering algorithms to graphs. Experiments in clustering protein structures show the benefits of the proposed theory.
Date of Conference: 01-08 June 2008
Date Added to IEEE Xplore: 26 September 2008
ISBN Information:

ISSN Information:

Conference Location: Hong Kong, China

I. Introduction

Graphs often occur as “natural” representations of structured objects in different application areas of machine learning. To adopt methods like central clustering or principal component analysis for graphs, an understanding of the structural version of the sample mean is imperative. But the concept of sample mean of graphs is hardly investigated, although a number of central clustering algorithms for graphs have been devised [1]–[3].

References

References is not available for this document.