Optimizing kernel alignment by data translation in feature space

Kernel-target alignment is commonly used to predict the behavior of any given reproducing kernel in a classification context, without training any kernel machine. However, a poor position of the data in feature space can drastically reduce the value of the alignment. This implies that, in a kernel selection setting, the best kernel in a given collection may be associated with a low value of alignment. In this paper, we present a new algorithm for maximizing the alignment by data translation in feature space. The aim is to reduce the biais introduced by the translation non-invariance of this criterion. Experimental results on multi-dimensional benchmarks show the effectiveness of our approach.