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Local Learning Projections

2007

Conference Paper

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This paper presents a Local Learning Projection (LLP) approach for linear dimensionality reduction. We first point out that the well known Principal Component Analysis (PCA) essentially seeks the projection that has the minimal global estimation error. Then we propose a dimensionality reduction algorithm that leads to the projection with the minimal local estimation error, and elucidate its advantages for classification tasks. We also indicate that LLP keeps the local information in the sense that the projection value of each point can be well estimated based on its neighbors and their projection values. Experimental results are provided to validate the effectiveness of the proposed algorithm.

Author(s): Wu, M. and Yu, K. and Yu, S. and Schölkopf, B.
Book Title: Proceedings of the 24th International Conference on Machine Learning
Pages: 1039-1046
Year: 2007
Month: June
Day: 0
Editors: Z Ghahramani
Publisher: ACM Press

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

DOI: 10.1145/1273496.1273627
Event Name: ICML 2007
Event Place: Corvallis, OR, USA

Address: New York, NY, USA
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{4460,
  title = {Local Learning Projections},
  author = {Wu, M. and Yu, K. and Yu, S. and Sch{\"o}lkopf, B.},
  booktitle = {Proceedings of the 24th International Conference on Machine Learning},
  pages = {1039-1046},
  editors = {Z Ghahramani},
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = jun,
  year = {2007},
  month_numeric = {6}
}