Lemma 23.1 (as proved): for every (U, W) there is V with VᵀV = I such that (V, Vᵀ) has PCA objective ≤ that of (U, W)
ProvedUnderstandingML.pca_reduction_lemmaleast-squaresorthonormalpca
Lemma 23.1. Let be a solution to Equation (23.1). Then the columns of are orthonormal () and .
As the proof establishes it: for every , there is with and , so (23.1) may be restricted to , (23.2). The literal statement is not what the proof gives ( has the same objective as ). .
Preamble
import Definitions.Def_UnderstandingML_DimReduction open MeasureTheory ProbabilityTheory
Formal statement
namespace UnderstandingML
/-- **Lemma 23.1** (p. 324), as its proof establishes it: for every `U ∈ ℝ^{d×n}` and
`W ∈ ℝ^{n×d}` there is a `V ∈ ℝ^{d×n}` with orthonormal columns (`VᵀV = I`) such that `(V, Vᵀ)`
does not increase the objective (23.1): `∑ᵢ ‖xᵢ − VVᵀxᵢ‖² ≤ ∑ᵢ ‖xᵢ − UWxᵢ‖²`. Hence (23.1) may
be restricted to `U` with orthonormal columns and `W = Uᵀ` (23.2). The book states the lemma as
a property of every solution `(U, W)`, which is not what the proof gives, since `(cU, W/c)` has
the same objective; `n ≤ d`. -/
theorem pca_reduction_lemma {m d n : ℕ} (hn : n ≤ d) (x : Fin m → Fin d → ℝ)
(U : Matrix (Fin d) (Fin n) ℝ) (W : Matrix (Fin n) (Fin d) ℝ) :
∃ V : Matrix (Fin d) (Fin n) ℝ, V.transpose * V = 1 ∧
pcaObjective x V V.transpose ≤ pcaObjective x U 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, §23.1 p. 324, Lemma 23.1 with its proof, and Equation (23.2)
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.