Kernelized convex clustering in RKHS with convergence guarantees, finite sample bounds, and empirical superiority on non-linear data.
That is, γ ′ wmin 2 ( n 2)X t=1 ∥At∗(β− ˆβ)∥ ≤ γ ′ 2 X i<j wij∥Aij∗(β− ˆβ)∥ Triangle inequality can finally be employed to get the final result as mentioned in the main paper
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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.