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Conference Paper

Local Learning Projections

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84321

Wu,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Wu, M., Yu K, Yu, S., & Schölkopf, B. (2007). Local Learning Projections. Proceedings of the 24th Annual International Conference on Machine Learning (ICML 2007), 1039-1046.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CD63-C
Abstract
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.