On Kernel Eigen-alignments of KRR: Reconstruction and Generalization
This paper investigates the critical role of eigenalignments between the kernel matrix and learning targets in achieving robust generalization in learning problems. We establish a direct connection between generalization performance in kernel methods and the estimation of eigenvectors and eigenvalues of matrices, offering a more intuitive understanding compared to prior work with minimal assumptions. We also show that, since the prediction task in KRR is essentially the weighted sum of eigenvectors/singular vectors, by analyzing how much error can be caused by perturbations to the kernel matrix, we can then derive a bound on this generalization error using the estimation stability of matr...
| Authors | Yang Liu, Ernest Fokoue, Richard Lange et al. |
|---|---|
| Primary category | stat.ML |
| Published | 14 May 2026 |
| Last updated at source | 19 September 2026 |
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