For multi-index polynomials, the top r eigenspace of the AGOP matrix from KRR recovers the central subspace at sample complexity n ~ d^{p+δ} where p is the degree of the informative component.
On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma
1 Pith paper cite this work, alongside 473 external citations. Polarity classification is still indexing.
1
Pith paper citing it
473
external citations · Crossref
fields
stat.ML 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Average Gradient Outer Product in kernel regression provably recovers the central subspace for multi-index models
For multi-index polynomials, the top r eigenspace of the AGOP matrix from KRR recovers the central subspace at sample complexity n ~ d^{p+δ} where p is the degree of the informative component.