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Optimizing kernel alignment by data translation in feature space

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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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Dates and versions

hal-02356501 , version 1 (08-11-2019)

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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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