Visualising kernel spaces
نویسندگان
چکیده
Classification in kernel machines consists of a nonlinear transformation of input data into a feature space, followed by a separation with a linear hyperplane. This transformation is expressed through a kernel function, which is capable of computing similarities between two data points in an abstract geometric space for which individual point vectors are computationally intractable. In this paper we combine the notion of kernel distance and methods for data dimensionality reduction to obtain visualisations for such kernel spaces.
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