Lean Ridgelet Blueprint

7. Further results from the source manuscript🔗

The following results appear in the arXiv src/03dev-*.tex files but lie beyond the current formalization of Chapters 2--5. They are recorded as unformalized nodes with their dependencies made explicit.

Theorem7.1
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A countable family of L^2 functions can be encoded isometrically into an orthonormal sequence in the null fiber. A suitable additive parameter perturbation reads a selected function into the visible component without changing the null component, and this perturbation is the unique one of minimum norm.

Definition1.22
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Theorem 1.23
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For a finite complex Radon measure \mu and a localized data measure \nu, define the measure-valued synthesis S_\nu[\mu] as the Bochner integral of the feature map.

Theorem7.2
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A null measure of total variation one can be approximated by an atomic measure of width N, whose output norm converges to zero at rate O(N^{-1/2}).

Lemma7.3
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Uniform Monte Carlo sampling on a bounded parameter domain for a finite measure separates the truncation error from the sampling error, and the latter has mean square O(N^{-1}).

Corollary1.24
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Theorem 1.21
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For a continuous activation of at most polynomial growth and a compactly supported data measure, every Schwartz ridgelet null element yields a finite-width null approximation.

Proposition1.25
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The parity of an activation gives an exact two-atom null relation. For ReLU, affine cancellation conditions give further exact null relations among finitely many neurons.