Structural interpretability in SVMs with truncated orthogonal polynomial kernels
We study post-training interpretability for Support Vector Machines (SVMs) built from truncated orthogonal polynomial kernels. Since the associated reproducing kernel Hilbert space is finite-dimensional and admits an explicit tensor-product orthonormal basis, the fitted decision function can be expanded exactly in intrinsic RKHS coordinates. This leads to Orthogonal Representation Contribution Analysis (ORCA), a diagnostic framework based on normalized Orthogonal Kernel Contribution (OKC) indices. These indices quantify how the squared RKHS norm of the classifier is distributed across interaction orders, total polynomial degrees, marginal coordinate effects, and pairwise contributions. Th...
| Authors | Víctor Soto-Larrosa, Nuria Torrado, Edmundo J. Huertas |
|---|---|
| Primary category | stat.ML |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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