Learning Kernels by Alignment for Multiclass Bayes Classification
Kernel methods separate data representation from decision-making, but typically require the kernel to be chosen in advance. We show that this kernel can instead be learned by alignment, and develop the resulting framework through the recently introduced Collaborative Learning and Inference (CLaI). We show that Collaborative Learning can be viewed as a kernel alignment process, in which an embedding is trained so that its induced similarity matches a label-derived target kernel. We also prove that Collaborative Inference is equivalent to kernel Bayes classification with Parzen-window density estimation. Motivated by these perspectives, we generalise CLaI by replacing cosine similarity with...
| Authors | Hollan Haule, Javier Escudero |
|---|---|
| Primary category | cs.LG |
| Published | 6 September 2026 |
| Last updated at source | 16 September 2026 |
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