Lemma 24.2: G(Q, θ) ≤ L(θ) for all Q ∈ 𝒬 with equality at the posterior Q_{i,y} = P_θ[Y = y | X = xᵢ], and the maximizers of G(Q, ·) and F(Q, ·) coincide
ProvedUnderstandingML.em_alternate_maxalternate-maximizationexpectation-maximizationjensen-inequality
Lemma 24.2. The EM procedure can be rewritten as , . Furthermore, .
Formally, for a positive joint: (i) for every (Jensen); (ii) the posterior attains , hence is a maximizer over ; (iii) for fixed , iff .
Preamble
import Definitions.Def_UnderstandingML_Generative open MeasureTheory ProbabilityTheory open scoped InnerProductSpace
Formal statement
namespace UnderstandingML
/-- **Lemma 24.2** (p. 351). The EM procedure can be rewritten as
`Q⁽ᵗ⁺¹⁾ = argmax_{Q ∈ 𝒬} G(Q, θ⁽ᵗ⁾)`, `θ⁽ᵗ⁺¹⁾ = argmax_θ G(Q⁽ᵗ⁺¹⁾, θ)`; furthermore
`G(Q⁽ᵗ⁺¹⁾, θ⁽ᵗ⁾) = L(θ⁽ᵗ⁾)`. Stated as: for every `Q ∈ 𝒬`, `G(Q, θ) ≤ L(θ)`; the posterior
`Q_{i,y} = P_θ[Y = y | X = xᵢ]` attains `G(Q, θ) = L(θ)`; and for fixed `Q` the maximizers of
`G(Q, ·)` and `F(Q, ·)` coincide. The joint is positive. -/
theorem em_alternate_max {Θ X : Type*} {k m : ℕ} (p : Θ → X → Fin k → ℝ)
(hp : ∀ θ x y, 0 < p θ x y) (x : Fin m → X) :
(∀ (Q : Fin m → Fin k → ℝ) (θ : Θ), IsRowStochastic Q → emG p x Q θ ≤ latentLogLik p x θ) ∧
(∀ θ : Θ, emG p x (fun i y ↦ posterior p θ (x i) y) θ = latentLogLik p x θ) ∧
(∀ (Q : Fin m → Fin k → ℝ) (θ θ' : Θ), emG p x Q θ ≤ emG p x Q θ' ↔ emF p x Q θ ≤ emF p x Q θ') := 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, §24.4.1 pp. 351-352, Lemma 24.2 with its proof
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.