Shorting Dynamics and Structured Kernel Regularization
This provides a unified operator-analytic approach for invariant kernel construction and structured regularization in data analysis, addressing domain-specific challenges.
The paper tackles the problem of removing the influence of specific feature subspaces while preserving structure elsewhere, resulting in a dynamic that converges to the classical shorted operator and enables a canonical form of kernel ridge regression with nuisance invariance.
This paper develops a nonlinear operator dynamic that progressively removes the influence of a prescribed feature subspace while retaining maximal structure elsewhere. The induced sequence of positive operators is monotone, admits an exact residual decomposition, and converges to the classical shorted operator. Transporting this dynamic to reproducing kernel Hilbert spaces yields a corresponding family of kernels that converges to the largest kernel dominated by the original one and annihilating the given subspace. In the finite-sample setting, the associated Gram operators inherit a structured residual decomposition that leads to a canonical form of kernel ridge regression and a principled way to enforce nuisance invariance. This gives a unified operator-analytic approach to invariant kernel construction and structured regularization in data analysis.