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  A Subspace Kernel for Nonlinear Feature Extraction

Wu, M., & Farquhar, J. (2007). A Subspace Kernel for Nonlinear Feature Extraction. In R. Sangal, H. Mehta, & R. Bagga (Eds.), IJCAI'07: 20th International Joint Conference on Artifical Intelligence (pp. 1125-1130). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CEF7-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-E8CA-4
Genre: Conference Paper

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IJCAI-2007-Wu.pdf (Any fulltext), 188KB
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https://dl.acm.org/citation.cfm?id=1625458 (Publisher version)
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 Creators:
Wu, M1, Author              
Farquhar, J1, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: Kernel based nonlinear Feature Extraction (KFE) or dimensionality reduction is a widely used pre-processing step in pattern classification and data mining tasks. Given a positive definite kernel function, it is well known that the input data are implicitly mapped to a feature space with usually very high dimensionality. The goal of KFE is to find a low dimensional subspace of this feature space, which retains most of the information needed for classification or data analysis. In this paper, we propose a subspace kernel based on which the feature extraction problem is transformed to a kernel parameter learning problem. The key observation is that when projecting data into a low dimensional subspace of the feature space, the parameters that are used for describing this subspace can be regarded as the parameters of the kernel function between the projected data. Therefore current kernel parameter learning methods can be adapted to optimize this parameterized kernel function. Experimental results are provided to validate the effectiveness of the proposed approach.

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 Dates: 2007-01
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 4159
 Degree: -

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Title: 20th International Joint Conference on Artificial Intelligence (IJCAI-07)
Place of Event: Hyderabad, India
Start-/End Date: 2007-01-06 - 2007-01-12

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Title: IJCAI'07: 20th International Joint Conference on Artifical Intelligence
Source Genre: Proceedings
 Creator(s):
Sangal, R, Editor
Mehta, H, Editor
Bagga, RK, Editor
Affiliations:
-
Publ. Info: San Francisco, CA, USA : Morgan Kaufmann Publishers Inc.
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1125 - 1130 Identifier: -