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Ranking on Data Manifolds

MPG-Autoren
/persons/resource/persons84330

Zhou,  D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84311

Weston,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83946

Gretton,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83824

Bousquet,  O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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MPIK-TR-113.pdf
(Verlagsversion), 787KB

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Zitation

Zhou, D., Weston, J., Gretton, A., Bousquet, O., & Schölkopf, B.(2003). Ranking on Data Manifolds (113). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-DC55-4
Zusammenfassung
The Google search engine has had a huge success with its PageRank web page ranking algorithm, which exploits global, rather than
local, hyperlink structure of the World Wide Web using random
walk. This algorithm can only be used for graph data, however.
Here we propose a simple universal ranking algorithm for vectorial
data, based on the exploration of the intrinsic global geometric
structure revealed by a huge amount of data. Experimental results
from image and text to bioinformatics illustrates the validity of
our algorithm.