On the Pre-Image Problem in Kernel Methods

BakIr, G; Schölkopf, B; Weston, J

doi:10.4018/978-1-59904-042-4.ch012

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On the Pre-Image Problem in Kernel Methods

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BakIr, G
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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

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Citation

BakIr, G., Schölkopf, B., & Weston, J. (2007). On the Pre-Image Problem in Kernel Methods. In G. Camps-Valls, J. Rojo-Álvarez, & M. Martínez-Ramón (Eds.), Kernel Methods in Bioengineering, Signal and Image Processing (pp. 284-302). Hershey, PA, USA: Idea Group Publishing.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CEFB-1

Abstract

In this chapter we are concerned with the problem of reconstructing patterns from their representation in feature space, known as the pre-image problem. We review existing algorithms and propose a learning based approach. All algorithms are discussed regarding their usability and complexity and evaluated on an image denoising application.