Abstract
We extend the framework of spatial autocorrelation analysis on Reproducing Kernel Hilbert Space (RKHS). Our results are based on the fact that some geometrical neighborhood structures vary when samples are mapped into a RKHS, while other neighborhood structures do not. These results allow us to design a new measure for measuring the goodness of a kernel and more generally a similarity matrix. Experiments on UCI datasets show the relevance of our methodology.
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Pisetta, V., Zighed, D.A. (2009). Similarity and Kernel Matrix Evaluation Based on Spatial Autocorrelation Analysis. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds) Foundations of Intelligent Systems. ISMIS 2009. Lecture Notes in Computer Science(), vol 5722. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04125-9_45
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DOI: https://doi.org/10.1007/978-3-642-04125-9_45
Publisher Name: Springer, Berlin, Heidelberg
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