PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning to Find Pre-Images
Goekhan BakIr, Jason Weston and Bernhard Schölkopf
In: NIPS 16, Vancouver(2004).


We consider the problem of reconstructing patterns from a feature map. Learning algorithms using kernels to operate in a reproducing kernel Hilbert space (RKHS) express their solutions in terms of input points mapped into the RKHS. We introduce a technique based on kernel principal component analysis and regression to reconstruct corresponding patterns in the input space (aka pre-images) and review its performance in several applications requiring the construction of pre-images. The introduced technique avoids difficult and/or unstable numerical optimization, is easy to implement and, unlike previous methods, permits the computation of pre-images in discrete input spaces.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:398
Deposited By:Bernhard Schölkopf
Deposited On:19 December 2004