Kernelized convex clustering in RKHS with convergence guarantees, finite sample bounds, and empirical superiority on non-linear data.
So, P[ϵ⊤atat ⊤ϵ≥(1 +δ 0)σ2∥at∥2]≤ 2 n 2 2 Letz 2 0 = maxt=1,...,( n 2)(1 +δ 0)σ2∥at∥2
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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.