Theorem 16.1 (Representer Theorem): if (16.2) has an optimal solution, some optimal solution is w = ∑ᵢ αᵢ ψ(xᵢ)
ProvedUnderstandingML.representer_theoremhilbert-spacekernel-methodsrepresenter-theorem
Theorem 16.1 (Representer Theorem). Assume that is a mapping from to a Hilbert space. Then, there exists a vector such that is an optimal solution of Equation (16.2), , where is monotonically nondecreasing.
Formally: assuming (16.2) has an optimal solution (as the book's proof does), with nondecreasing on .
Preamble
import Definitions.Def_UnderstandingML_Kernel open MeasureTheory open scoped InnerProductSpace
Formal statement
universe u
namespace UnderstandingML
/-- **Theorem 16.1 (Representer Theorem)** (p. 218). Assume that `ψ` is a mapping from `X` to a
Hilbert space, and that the problem (16.2), `min_w f(⟨w, ψ(x₁)⟩, …, ⟨w, ψ(x_m)⟩) + R(‖w‖)` with
`R` nondecreasing, has an optimal solution. Then there exists a vector `α ∈ ℝ^m` such that
`w = ∑ᵢ αᵢ ψ(xᵢ)` is an optimal solution of Equation (16.2). -/
theorem representer_theorem {X : Type u} {F : Type*} [NormedAddCommGroup F]
[InnerProductSpace ℝ F] [CompleteSpace F] {m : ℕ} (ψ : X → F) (x : Fin m → X)
(f : (Fin m → ℝ) → ℝ) (R : ℝ → ℝ) (hR : MonotoneOn R (Set.Ici 0)) (wstar : F)
(hopt : ∀ w, kernelObjective ψ x f R wstar ≤ kernelObjective ψ x f R w) :
∃ α : Fin m → ℝ, ∀ w,
kernelObjective ψ x f R (∑ i, α i • ψ (x i)) ≤ kernelObjective ψ x f R w := by sorry
end UnderstandingML
Source
Shalev-Shwartz and Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press 2014, doi:10.1017/CBO9781107298019, §16.2 p. 218, Theorem 16.1 with its proof
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.