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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 352319.0, MPI für biologische Kybernetik / Biologische Kybernetik
Local Learning Projections
Authors:Wu, M.; Yu, K.; Yu, S.; Schölkopf, B.
Date of Publication (YYYY-MM-DD):2007-06
Title of Proceedings:Proceedings of the 24th International Conference on Machine Learning (ICML 2007)
Start Page:1039
End Page:1046
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:4460
URL:http://portal.acm.org/citation.cfm?id=1273627
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