Kernelized convex clustering in RKHS with convergence guarantees, finite sample bounds, and empirical superiority on non-linear data.
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A New Framework for Convex Clustering in Kernel Spaces: Finite Sample Bounds, Consistency and Performance Insights
Kernelized convex clustering in RKHS with convergence guarantees, finite sample bounds, and empirical superiority on non-linear data.