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Transductive Classification via Local Learning Regularization

2007

Conference Paper

ei


The idea of local learning, classifying a particular point based on its neighbors, has been successfully applied to supervised learning problems. In this paper, we adapt it for Transductive Classification (TC) problems. Specifically, we formulate a Local Learning Regularizer (LL-Reg) which leads to a solution with the property that the label of each data point can be well predicted based on its neighbors and their labels. For model selection, an efficient way to compute the leave-one-out classification error is provided for the proposed and related algorithms. Experimental results using several benchmark datasets illustrate the effectiveness of the proposed approach.

Author(s): Wu, M. and Schölkopf, B.
Book Title: JMLR Workshop and Conference Proceedings Volume 2: AISTATS 2007
Journal: Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS 2007)
Pages: 628-635
Year: 2007
Month: March
Day: 0
Editors: M Meila and X Shen

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

Event Name: 11th International Conference on Artificial Intelligence and Statistics
Event Place: San Juan, Puerto Rico

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{4271,
  title = {Transductive Classification via Local Learning Regularization},
  author = {Wu, M. and Sch{\"o}lkopf, B.},
  journal = {Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS 2007)},
  booktitle = {JMLR Workshop and Conference Proceedings Volume 2: AISTATS 2007},
  pages = {628-635},
  editors = {M Meila and X Shen},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = mar,
  year = {2007},
  month_numeric = {3}
}