School of Electronics and Computer Science, University of Southampton, Southampton, England, 2005
Abstract
Kernel methods make it relatively easy to define complex highdimensional
feature spaces. This raises the question of how we can
identify the relevant subspaces for a particular learning task. When two
views of the same phenomenon are available kernel Canonical Correlation
Analysis (KCCA) has been shown to be an effective preprocessing
step that can improve the performance of classification algorithms such
as the Support Vector Machine (SVM). This paper takes this observation
to its logical conclusion and proposes a method that combines this
two stage learning (KCCA followed by SVM) into a single optimisation
termed SVM-2K. We present both experimental and theoretical analysis
of the approach showing encouraging results and insights