Two adaptive kernel selection techniques for Kernelized Diffusion Maps are developed, backed by proofs of Lipschitz dependence on kernel weights, spectral projector continuity under gap conditions, residual control, and exponential consistency of the selector.
Learning ``Best'' Kernels from Data in
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
stat.ML 1years
2026 1verdicts
UNVERDICTED 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
Adaptive Kernel Selection for Kernelized Diffusion Maps
Two adaptive kernel selection techniques for Kernelized Diffusion Maps are developed, backed by proofs of Lipschitz dependence on kernel weights, spectral projector continuity under gap conditions, residual control, and exponential consistency of the selector.