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Optimizing kernel alignment by data translation in feature space

Jean-Baptiste Pothin 1 Cédric Richard 1
1 M2S - Modélisation et Sûreté des Systèmes
ISTIT - Institut des Sciences et Technologies de l'Information de Troyes
Abstract : 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.
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Conference papers
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https://hal-utt.archives-ouvertes.fr/hal-02356501
Contributor : Jean-Baptiste Vu Van <>
Submitted on : Friday, November 8, 2019 - 5:48:39 PM
Last modification on : Saturday, November 9, 2019 - 2:14:20 AM

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Jean-Baptiste Pothin, Cédric Richard. Optimizing kernel alignment by data translation in feature space. ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, Mar 2008, Las Vegas, United States. pp.3345-3348, ⟨10.1109/ICASSP.2008.4518367⟩. ⟨hal-02356501⟩

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