||Kernel Methods for De-noising with Neuroimaging Application
||Abrahamsen, Trine Julie (Intelligent Signal Processing, Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
||Hansen, Lars Kai (Intelligent Signal Processing, Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
||Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark
||This thesis examines the use of kernel methods for non-linear data analysis. In
particular kernel principal component analysis (kPCA) is used for de-noising. In
this context, solution of the pre-image problem is a key element to efficient denoising.
Pre-image estimation is inherently ill-posed for many common choices
of kernel function. In this thesis it is shown, how many of the often used estimation
schemes lack stability. A new pre-image estimation method for de-noising
is proposed, by including input space distance regularization. By extensive experiments
on handwritten digits from the USPS data set, the new method is
compared to three of the widely used schemes. Thereby it is shown how the previous
methods deteriorate when the feature space mapping is very non-linear.
However, by the new input space distance regularization approach the variability
is reduced with very limited sacrifice in terms of de-noising efficiency.
The pre-image methodology has furthermore been successfully applied to real
world biomedical data analysis. Using data from the Center for Integrated
Molecular Brain Imaging (Cimbi), it is investigated how kernel PCA de-noising
enhances personality trait and brain function correlations. The data set include
regional binding potentials (BPs) of the serotonin receptor subtype 5-HT2A and
the scores from a NEO-PI-R personality assessment. Finally, it is demonstrated
how a notable improvement of the correlations between frontolimbic 5-HT2A receptor
BP and the traits neuroticism, anxiety, and vulnerability can be achieved
by kernel PCA de-noising.
||Technical University of Denmark (DTU) : Kgs. Lyngby, Denmark
Creation date: 2009-08-19
Update date: 2009-11-04