%0 Conference Proceedings %T Optimizing kernel alignment by data translation in feature space %+ Modélisation et Sûreté des Systèmes (M2S) %A Pothin, Jean-Baptiste %A Richard, Cédric %< avec comité de lecture %B ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing %C Las Vegas, United States %I IEEE %3 ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing %P 3345-3348 %8 2008-03-31 %D 2008 %R 10.1109/ICASSP.2008.4518367 %K kernel alignment %K data translation %K SVM %Z Engineering Sciences [physics]/Signal and Image processingConference papers %X 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. %G English %L hal-02356501 %U https://utt.hal.science/hal-02356501 %~ CNRS %~ UNIV-TROYES %~ UTT %~ UTT-LIST3N %~ LM2S-UTT