Pack only the essentials: Adaptive dictionary learning for kernel ridge regression
One of the major limits of kernel ridge regression (KRR) is that storing and manipulating the kernel matrix K_n for n samples requires O(n^2) space, which rapidly becomes unfeasible for large n. Nystrom approximations reduce the space complexity to O(nm) by sampling m columns from K_n. Uniform sampling preserves KRR accuracy (up to epsilon) only when m is proportional to the maximum degree of freedom of K_n, which may require O(n) columns for datasets with high coherence. Sampling columns according to their ridge leverage scores (RLS) gives accurate Nystrom approximations with m proportional to the effective dimension, but computing exact RLS also requires O(n^2) space. (Calandriello et a...
| Authors | Daniele Calandriello, Alessandro Lazaric, Michal Valko |
|---|---|
| Primary category | stat.ML |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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