Input Space Regularization Stabilizes Pre-images for Kernel PCA De-noising |
Trine Julie Julie Abrahamsen, Lars Kai Hansen
|
| Abstract | 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. |
| Keywords | Kernel PCA, Pre-image, De-noising |
| Type | Conference paper [With referee] |
| Conference | IEEE Workshop on Machine Learning for Signal Processing (MLSP) |
| Year | 2009 |
| BibTeX data | [bibtex] |
| IMM Group(s) | Intelligent Signal Processing |