Solution of the pre-image problem is key to efficient nonlinear de-noising using kernel Principal Component Analysis. Pre-image estimation is inherently ill-posed for typical kernels used in applications and consequently the most widely used estimation schemes lack stability. For de-noising applications we propose input space distance regularization as a stabilizer for pre-image estimation. We perform extensive experiments on the USPS digit modeling problem to evaluate the stability of three widely used pre-image estimators. We show that the previous methods lack stability when the feature mapping is non-linear, however, by applying a simple input space distance regularizer we can reduce variability with very limited sacrifice in terms of de-noising efficiency.
|Title of host publication||IEEE International Workshop on Machine Learning for Signal Processing, 2009. MLSP 2009.|
|Publication status||Published - 2009|
|Event||2009 IEEE International Workshop on Machine Learning for Signal Processing - Grenoble, France|
Duration: 2 Sep 2009 → 4 Sep 2009
|Workshop||2009 IEEE International Workshop on Machine Learning for Signal Processing|
|Period||02/09/2009 → 04/09/2009|
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- Kernel PCA