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Ranking on Data Manifolds


Conference Paper


The Google search engine has enjoyed a huge success with its web page ranking algorithm, which exploits global, rather than local, hyperlink structure of the web using random walks. Here we propose a simple universal ranking algorithm for data lying in the Euclidean space, such as text or image data. The core idea of our method is to rank the data with respect to the intrinsic manifold structure collectively revealed by a great amount of data. Encouraging experimental results from synthetic, image, and text data illustrate the validity of our method.

Author(s): Zhou, D. and Weston, J. and Gretton, A. and Bousquet, O. and Schölkopf, B.
Book Title: Advances in neural information processing systems 16
Journal: Advances in Neural Information Processing Systems
Pages: 169-176
Year: 2004
Month: June
Day: 0
Editors: S Thrun and L Saul and B Sch{\"o}lkopf
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: 17th Annual Conference on Neural Information Processing Systems (NIPS 2003)
Event Place: Vancouver, BC, Canada

Address: Cambridge, MA, USA
Digital: 0
Institution: Max Planck Institute for Biological Cybernetics
ISBN: 0-262-20152-6
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Ranking on Data Manifolds},
  author = {Zhou, D. and Weston, J. and Gretton, A. and Bousquet, O. and Sch{\"o}lkopf, B.},
  journal = {Advances in Neural Information Processing Systems},
  booktitle = {Advances in neural information processing systems 16},
  pages = {169-176},
  editors = {S Thrun and L Saul and B Sch{\"o}lkopf},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = jun,
  year = {2004},
  month_numeric = {6}