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  Local Learning Projections

Wu, M., Yu, K., Yu, S., & Schölkopf, B. (2007). Local Learning Projections. In Z. Ghahramani (Ed.), ICML '07: 24th International Conference on Machine Learning (pp. 1039-1046). New York, NY, USA: ACM Press.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CD63-C Version Permalink: https://hdl.handle.net/21.11116/0000-0003-E25E-5
Genre: Conference Paper

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ICML-2007-Wu.pdf (Any fulltext), 204KB
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https://dl.acm.org/citation.cfm?doid=1273496.1273627 (Publisher version)
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 Creators:
Wu, M1, 2, Author           
Yu , K, Author
Yu, S, Author
Schölkopf, B1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2007-06
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1145/1273496.1273627
BibTex Citekey: 4460
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Title: 24th International Conference on Machine Learning (ICML 2007)
Place of Event: Corvallis, OR, USA
Start-/End Date: 2007-06-20 - 2007-06-24

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Title: ICML '07: 24th International Conference on Machine Learning
Source Genre: Proceedings
 Creator(s):
Ghahramani, Z, Editor
Affiliations:
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Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1039 - 1046 Identifier: ISBN: 978-1-59593-793-3