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Tikhonov regularisation scales each singular component by the filter factor
Statement
Let and let have singular value decomposition , and let . If is the Tikhonov minimiser for this and at a parameter , then
Thus the th singular component is multiplied by the filter factor .
Facts & Assumptions
Given: A scalar field , a singular value decomposition , a right-hand side , and a parameter .
admits a singular value decomposition with left singular vectors and right singular vectors (Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition).
The Tikhonov minimiser is (For every , the Tikhonov objective is strictly convex and has the unique minimiser ).
Proof
Using [L2] and the SVD from [L1],
The diagonal matrix has diagonal entries in the nonzero singular directions and in the zero singular directions. Therefore
This is exactly the stated filter-factor formula.
Depends on
Used by
- Ridge regularisation damps the small-singular-value directions in a diagonal inverse problem Example
- Truncated SVD and Tikhonov regularisation act as hard and smooth spectral filters on the singular components Proposition
- As λ↓0, the Tikhonov minimisers converge to the Moore--Penrose solution A^+b Theorem
Dependency tree · two levels
7 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Stanford CS205L, Unit 12: Regularization (standard reference, not scraped)