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Transductive Inference with Graphs


Technical Report


We propose a general regularization framework for transductive inference. The given data are thought of as a graph, where the edges encode the pairwise relationships among data. We develop discrete analysis and geometry on graphs, and then naturally adapt the classical regularization in the continuous case to the graph situation. A new and effective algorithm is derived from this general framework, as well as an approach we developed before.

Author(s): Zhou, D. and Schölkopf, B.
Year: 2004
Day: 0

Department(s): Empirical Inference
Bibtex Type: Technical Report (techreport)

Institution: Max Planck Institute for Biological Cybernetics

Note: See the improved version Regularization on Discrete Spaces.
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik


  title = {Transductive Inference with Graphs},
  author = {Zhou, D. and Sch{\"o}lkopf, B.},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics},
  school = {Biologische Kybernetik},
  year = {2004},
  note = {See the improved version Regularization on Discrete Spaces.}