A feasible k-means kernel trick under non-Euclidean feature space - Kłopotek, Robert; Kłopotek, Mieczysław; Wierzchoń, Sławomir T. (Zielona Góra: Uniwersytet Zielonogórski)
This paper poses the question of whether or not the usage of the kernel trick is justified. We investigate it for the special case of its usage in the kernel k-means algorithm. Kernel-k-means is a clustering algorithm, allowing clustering data in a similar way to k-means when an embedding of data points into Euclidean space is not provided and instead a matrix of "distances" (dissimilarities) or similarities is available. The kernel trick allows us to by-pass the need of finding an embedding into Euclidean space.
Keywords: kernel methods; k-means; clustering; non-Euclidean feature space; Gower/Legendre theorem