Algorithms for the Sample Mean of Graphs

Authors:
Brijnesh J. Jain;Klaus Obermayer
Affiliations:
Berlin Institute of Technology, Germany;Berlin Institute of Technology, Germany
Venue:
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Year:
2009

Citing 0
Cited 4

Elkan's k-means algorithm for graphs

MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Maximum likelihood for gaussians on graphs

GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Generalized learning graph quantization

GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Maximum likelihood method for parameter estimation of bell-shaped functions on graphs

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

Measures of central tendency for graphs are important for protoype construction, frequent substructure mining, and multiple alignment of protein structures. This contribution proposes subgradient-based methods for determining a sample mean of graphs. We assess the performance of the proposed algorithms in a comparative empirical study.