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
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ISSN Information:

Conference Location: Hong Kong, China
Citations are not available for this document.

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].

Cites in Papers - |

Cites in Papers - IEEE (2)

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1.
Brijnesh Jain, Klaus Obermayer, "Consistent Estimator of Median and Mean Graph", 2010 20th International Conference on Pattern Recognition, pp.1032-1035, 2010.
2.
Itziar Bardaji, Miquel Ferrer, Alberto Sanfeliu, "Computing the Barycenter Graph by Means of the Graph Edit Distance", 2010 20th International Conference on Pattern Recognition, pp.962-965, 2010.

Cites in Papers - Other Publishers (8)

1.
Guangda Liu, Tanmay Nath, Gerit A. Linneweber, Annelies Claeys, Zhengyu Guo, Jin Li, Mercedes Bengochea, Steve De Backer, Barbara Weyn, Manu Sneyders, Hans Nicasy, Peng Yu, Paul Scheunders, Bassem A. Hassan, "A simple computer vision pipeline reveals the effects of isolation on social interaction dynamics in Drosophila", PLOS Computational Biology, vol.14, no.8, pp.e1006410, 2018.
2.
Brijnesh J. Jain, "On the geometry of graph spaces", Discrete Applied Mathematics, vol.214, pp.126, 2016.
3.
Brijnesh J. Jain, "Statistical graph space analysis", Pattern Recognition, vol.60, pp.802, 2016.
4.
Brijnesh J. Jain, Klaus Obermayer, "Graph quantization", Computer Vision and Image Understanding, vol.115, no.7, pp.946, 2011.
5.
Brijnesh J. Jain, Klaus Obermayer, Structural, Syntactic, and Statistical Pattern Recognition, vol.6218, pp.690, 2010.
6.
Itziar Bardaji, Miquel Ferrer, Alberto Sanfeliu, Structural, Syntactic, and Statistical Pattern Recognition, vol.6218, pp.149, 2010.
7.
Brijnesh J. Jain, Klaus Obermayer, Advances in Soft Computing, vol.6438, pp.22, 2010.
8.
Brijnesh J. Jain, Klaus Obermayer, Computer Analysis of Images and Patterns, vol.5702, pp.351, 2009.

References

References is not available for this document.