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Ranking on Data Manifolds
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.
@techreport{2290, title = {Ranking on Data Manifolds}, abstract = {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.}, number = {113}, organization = {Max-Planck-Gesellschaft}, institution = {Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany}, school = {Biologische Kybernetik}, month = jun, year = {2003}, slug = {2290}, author = {Zhou, D. and Weston, J. and Gretton, A. and Bousquet, O. and Sch{\"o}lkopf, B.}, month_numeric = {6} }