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  Sparse Kernel Feature Analysis

Smola, A., Mangasarian, O., & Schölkopf, B.(1999). Sparse Kernel Feature Analysis (99-04). Madison, WI, USA: University of Wisconsin, Data Mining Institute.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-C4F8-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-C4F9-5
Genre: Report

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 Creators:
Smola, AJ, Author              
Mangasarian, OL, Author
Schölkopf, B1, Author              
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1External Organizations, ou_persistent22              

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 Abstract: Kernel Principal Component Analysis (KPCA) has proven to be a versatile tool for unsupervised learning, however at a high computational cost due to the dense expansions in terms of kernel functions. We overcome this problem by proposing a new class of feature extractors employing ` 1 norms in coefficient space instead of the reproducing kernel Hilbert space in which KPCA was originally formulated in. Moreover, the modified setting allows us to efficiently extract features maximizing criteria other than the variance much in a projection pursuit fashion.

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 Dates: 1999-10
 Publication Status: Published in print
 Pages: 21
 Publishing info: Madison, WI, USA : University of Wisconsin, Data Mining Institute
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: 99-04
 Degree: -

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