Gaussian processes (GP) provide a kernel machine framework. They have been mainly applied to regression and classification. We propose a pseudo-density estimation method based on the information of variance functions of GPs, which relates to the density of the data points. We also show how the constructed pseudo-density can be applied to clustering. Through simulation we show that the topology of the pseudo-density represents the clustering information well with promising results. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Kim, H. C., & Lee, J. (2006). Pseudo-density estimation for clustering with Gaussian processes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3971 LNCS, pp. 1238–1243). Springer Verlag. https://doi.org/10.1007/11759966_183
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