Unbiased Risk Estimation as Parameter Choice Rule for Filter-based Regularization Methods

Transcription

1 Unbiased Risk Estimation as Parameter Choice Rule for Filter-based Regularization Methods Frank Werner 1 Statistical Inverse Problems in Biophysics Group Max Planck Institute for Biophysical Chemistry, Göttingen and Felix Bernstein Institute for Mathematical Statistics in the Biosciences University of Göttingen Chemnitz Symposium on Inverse Problems 2017 (on Tour in Rio) 1 joint work with Housen Li Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

2 Outline 1 Introduction 2 A posteriori parameter choice methods 3 Error analysis 4 Simulations 5 Conclusion Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

3 Introduction Outline 1 Introduction 2 A posteriori parameter choice methods 3 Error analysis 4 Simulations 5 Conclusion Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

4 Introduction Statistical inverse problems Setting: X, Y Hilbert spaces, T : X Y bounded, linear Task: Recover unknown f X from noisy measurements Y = Tf + σξ Noise: ξ is a standard Gaussian white noise process, σ > 0 noise level The model has to be understood in a weak sense: with ξ, g N Y g := Tf, g Y + σ ξ, g for all g Y ( ) 0, g 2 Y and E [ ξ, g 1 ξ, g 2 ] = g 1, g 2 Y. Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

5 Introduction Statistical inverse problems Assumptions: T is injective and Hilbert-Schmidt ( σ 2 k <, σ k singular values) σ is known exactly As the problem is ill-posed, regularization is needed. Consider filter-based regularization schemes ˆf α := q α (T T )T Y, α > 0. Aim: A posteriori choice of α such that rate of convergence (as σ 0) is order optimal (no loss of log-factors) Note: Heuristic parameter choice rules might work here as well, as the Bakushinskĭı veto does not hold in our setting (Becker 11). Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

6 A posteriori parameter choice methods Outline 1 Introduction 2 A posteriori parameter choice methods 3 Error analysis 4 Simulations 5 Conclusion Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

7 A posteriori parameter choice methods The discrepancy principle { For deterministic data: α DP = max α > 0 } T ˆf α Y τσ Y But here: Y / Y! Either pre-smoothing (Y Z := T Y X ) or discretization: Y R n, ξ N n (0, I n ) and choose { α DP = max α > 0 T ˆfα Y τσ } n 2 Pros: Easy to implement Works for all q α Order-optimal convergence rates Cons: How to choose τ 1? Only discretized meaningful Early saturation Davies & Anderssen 86, Lukas 95, Blanchard, Hoffmann & Reiß 16 Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

8 A posteriori parameter choice methods The quasi-optimality criterion Neubauer 08 (r α (λ) = 1 λq α (λ)): α QO = argmin α>0 But for spectral cut-off r α (T T ) ˆf α = 0 for all α > 0 r α (T X T ) ˆf α Alternative formulation for Tikhonov regularization if candidates α 1 <... < α m are given: n QO = argmin ˆf αn ˆf X αn+1, α QO := α nqo. Pros: 1 n m 1 Easy to implement, very fast No knowledge of σ necessary Order-optimal convergence rates in mildly ill-posed situations Cons: Only for special q α Additional assumptions on noise and/or f necessary Performance unclear in severely ill-posed situations Bauer & Kindermann 08, Bauer & Reiß 08, Bauer & Kindermann 09 Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

9 A posteriori parameter choice methods The Lepskĭı-type balancing principle For given α, the standard deviation of ˆf α can be bounded by ( ) std (α) := σ Tr q αk (T T ) 2 T T If candidates α 1 <... < α m are given: { n LEP = max j } X ˆf αj ˆf αk 4κstd (α k ) for all 1 k j Pros: and α LEP = α nlep Works for all q α Robust in practice convergence rates (mildly / severely ill-posed) Cons: Computationally expansive κ 1 depends on decay of σ k loss of log factor compared to order-optimal rate Bauer & Pereverzev 05, Mathé 06, Mathé & Pereverzev 06 Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

10 A posteriori parameter choice methods Unbiased risk estimation Dating back to ideas of Mallows 73 and Stein 81 let ˆr (α, Y ) := T ˆf 2 α 2 T ˆf α, Y + 2σ 2 Tr (T Tq α (T T )) Y and choose α URE = argmin α>0 ˆr (α, Y ) [ T Note that E [ˆr (α, Y )] = E ˆf α Tf of α (Unbiased Risk Estimation) 2 Y ] c with c independent For spectral cut-off and in mildly ill-posed situations, this gives order optimal rates (Chernousova & Golubev 14). Besides this, only optimality in the image space is known (Li 87, Lukas 93, Kneip 94). Distributional behavior of α URE : Lucka et al 17. In general: Pros? Cons? Convergence rates? Order optimality? Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

11 Error analysis Outline 1 Introduction 2 A posteriori parameter choice methods 3 Error analysis 4 Simulations 5 Conclusion Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

12 Error analysis A priori parameter choice Assumptions Filter α q α (λ) C q and λ q α (λ) C q. Source condition W φ := { f X f = φ(t T )w, ω X C }. Note: for any f X there exists φ such that f W φ. Qualification condition The function φ is a qualification of q α if sup φ(λ) 1 λq α (λ) C φ φ(α) λ [0, T T Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

13 Error analysis A priori parameter choice Assumptions Let Σ (x) := # { k σ 2 k x} be the counting function of the singular values of T Approximation by smooth surrogate There exists S C 2, α 1 (0, T T ] and C S (0, 2) such that (1) lim α 0 S(α)/Σ(α) = 1 (approximation) (2) S < 0 (decreasing) (3) lim α S(α) = lim α S (α) = 0 (behavior above σ1 2) (4) lim α 0 αs(α) = 0 (Hilbert-Schmidt) (5) αs (α) is integrable on (0, α 1 ] S (6) (α) S (α) C S α on (0, α 1 ] Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

14 Error analysis A priori parameter choice A priori convergence rates Bissantz, Hohage, Munk, Ruymgaart 07 Let α φ(α ) 2 = σ 2 S(α ). (i) If φ is a qualification of q α, then [ ] ˆf α f 2 X sup E f W φ φ(α ) 2 = σ 2 S(α ) α as σ 0. (ii) If λ λφ(λ) is a qualification of the filter q α, then [ T ] sup E ˆf α Tf 2 α φ(α ) 2 = σ 2 S(α ) as σ 0. f W φ Y Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

15 Error analysis A priori parameter choice Mildly ill-posed situation: Example Assume σ 2 k k a, W b := Bissantz, Hohage, Munk, Ruymgaart 07 Let α (σ 2 ) a/(a+b+1). { f X : k=1 kb f 2 k 1 } with a > 1, b > 0: If φ (λ) = λ b/2a is a qualification of q α, then [ ] sup E ˆf α f 2 (σ 2 ) f W b X b a+b+1. If φ (λ) = λ b/2a+1/2 is a qualification of q α, then [ T ] sup E ˆfα Tf 2 (σ 2 ) a+b a+b+1. f W b Y These rates are order optimal over W b. Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

16 Error analysis Unbiased Risk estimation as parameter choice Unbiased risk estimation vs. the oracle Recall that ˆr (α, Y ) := 2 T ˆf α 2 T ˆf α, Y + 2σ 2 Tr (T Tq α (T T )) Y is an unbiased estimator for [ T r (α, f ) := E ˆfα Tf In the following, we will compare α URE = argmin α>0 2 Y ]. ˆr (α, Y ) and α o = argmin r(α, f ). α>0 Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

17 Error analysis Unbiased Risk estimation as parameter choice Additional assumptions (a) α {q α (σ 2 k )} k=1 is strictly monotone and continuous as R l2. (b) As α 0, αq α (α) c q > 0. (c) For α > 0, the function λ λq α (λ) is non-decreasing. Satisfied by Tikhonov, spectral cut-off, Landweber, iterated Tikhonov and Showalter regularization, under proper parametrization. E.g. Tikhonov with re-parametrization α α (q α (λ) = 1/( α + λ)) violates (b). ( λ ) (d) ψ (λ) := λφ 1 is convex (e) There exists a constant C q > c 2 q 1 ( Ψ (C q x) exp C for some explicitly known C > 0. such that ) x dx < with Ψ(x) := 2 x (S 1 (x)) 2. (d) can always be satisfied by weakening φ, (e) restricts the decay of the singular values Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

18 Error analysis Unbiased Risk estimation as parameter choice Oracle inequality Li & W. 16 There are positive constants C i, i = 1,..., 6, such that for all f W φ it holds [ ] E ˆfαURE f 2 C 1 ψ 1( C 2 r(α o, f ) + C 3 σ 2) + C 4 σ 2 X as σ 0. r(α o, f ) + σ r(α o, f ) + C 5 ( ) S 1 r(α C o,f ) 6 σ 2 Gives a comparison of the strong risk under α URE with the weak risk under the oracle α o. Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

19 Error analysis Unbiased Risk estimation as parameter choice Convergence rates Li & W. 16 If also λ λφ(λ) is a qualification of the filter q α, then for α φ(α ) 2 = σ 2 S(α ) there are C 1, C 2, C 3 > 0 such that as σ 0. [ ] sup E ˆf f 2 αure C 1 σ 2 S(α ) σ 2 S(α ) + C 2 f W φ X α S 1 (C 3 S(α )) If there is C 4 > 0 such that S(C 4 x) C 3 S(x), then this equals the a priori rate [ ] sup E ˆfαURE f 2 φ(α ) 2 = σ 2 S(α ). f W φ X α Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

20 Error analysis Unbiased Risk estimation as parameter choice Order optimality in mildly ill-posed situations Assume σ 2 k k a, W b := Oracle inequality For all f W: [ ] E ˆf f 2 αure X { f X : k=1 kb f 2 k 1 } with a > 1, b > 0: r(α o, f ) b a+b + σ 2a r(α o, f ) 1+a + σ 1 2a r(α o, f ) 1+2a 2. Convergence rate Thus, if λ λ b/2a+1/2 is a qualification of q α, then [ ] sup E ˆfαURE f 2 σ 2b a+b+1 f W b X which is order-optimal. Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

21 Error analysis Unbiased Risk estimation as parameter choice Unbiased risk estimation - pros and cons [ T 2 ] α URE = argmin ˆf α 2 T ˆf α, Y + 2σ 2 Tr (T Tq α (T T )) α>0 Y Pros: Works for many q α order-optimal convergence rates in mildly ill-posed situations no loss of log factor no tuning parameter Cons: Computationally expansive Early saturation performance in severely ill-posed situations unclear H. Li and F. Werner (2017). Empirical risk minimization as parameter choice rule for general linear regularization methods. Submitted, arxiv: Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

22 Simulations Outline 1 Introduction 2 A posteriori parameter choice methods 3 Error analysis 4 Simulations 5 Conclusion Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

23 Simulations Rates of convergence A mildly ill-posed situation - antiderivative Let T : L 2 ([0, 1]) L 2 ([0, 1]) given by (Tf ) (x) = 1 0 min {x (1 y), y (1 x)} f (y) dy As (Tf ) = f the singular values σ k of T satisfy σ k k 2. We choose f (x) = Fourier coefficients: f k = ( 1)k 1 4π 3 k 2 ε > 0. { x if 0 x 1 2, 1 x if 1 2 x 1., so the optimal rate is O ( σ 3 4 ε) for any Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

24 Simulations Rates of convergence A mildly ill-posed situation - Tikhonov regularization 10 2 Empirical MISE 10 2 Empirical Variance 10 0 σ Figure: Empirical MISE and variance of ˆf f 2 2 over 104 repetitions: α o ( ), α DP ( ), α QO ( ), α LEP ( ), α URE ( ). Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

25 Simulations Rates of convergence A severely ill-posed situation - satellite gradiometry Let R > 1 and S R 2 the unit sphere. Given g = 2 u r 2 u = 0 in R d \ B, u = f on S, ( u (x) = O x 1 2 ) as x 2. Corresponding T : L 2 (S, µ) L 2 (RS, µ) has singular values σ k = k ( k + 1) R k 2. on RS find f in We choose f (x) = π 2 x, x [ π, π] ( Optimal rate of convergence is O ( log (σ)) 3+ε) for any ε > 0. Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

26 Simulations Rates of convergence A severely ill-posed situation - Tikhonov regularization Empirical MISE 10 0 Empirical Variance ( log (σ)) Figure: Empirical MISE and variance of ˆf f 2 2 over 104 repetitions: α o ( ), α DP ( ), α QO ( ), α LEP ( ), α URE ( ). Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

27 Simulations Rates of convergence A severely ill-posed situation - backwards heat equation Let t > 0. Given g = u (, t) find f in u t (x, t) = 2 u (x, t) in ( π, π] (0, t), t 2 u (x, 0) = f (x) on [ π, π], u ( π, t) = u (π, t) on t (0, t]. Corresponding T : L 2 ([ π, π]) L 2 ([ π, π]) has singular values σ k = exp ( k 2 t ). We choose f (x) = π 2 x, x [ π, π] ( Optimal rate of convergence is O ( log (σ)) 3/2+ε) for any ε > 0. Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

28 Simulations Rates of convergence A severely ill-posed situation - Tikhonov regularization 10 1 Empirical MISE 10 2 Empirical Variance 10 0 ( log (σ)) Figure: Empirical MISE and variance of ˆf f 2 2 over 104 repetitions: α o ( ), α DP ( ), α QO ( ), α LEP ( ), α URE ( ). Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

29 Simulations Efficiency simulations Efficiency simulations Measure the efficiency of a parameter choice rule α by the fraction [ ] E ˆfαo f 2 X R := [ ] E ˆf α f Numerical approximations of these as functions of σ with different parameters a, ν > 0 in the following setting: σ k = exp ( ak) f k = ±k ν (1 + N ( 0, 0.1 2)) Y k = σ k f k + N ( 0, σ 2) k = 1,..., 300, 10 4 repetitions Tikhonov regularization 2 X Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

30 Simulations Efficiency simulations Efficiency simulations - results a = 0.2, ν = 1 a = 0.3, ν = σ σ Figure: R QO ( ), R DP ( ), R LEP ( ), and R URE ( ) Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

31 Simulations Efficiency simulations Efficiency simulations - results a = 0.4, ν = 1 a = 0.6, ν = σ σ Figure: R QO ( ), R DP ( ), R LEP ( ), and R URE ( ) Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

32 Simulations Efficiency simulations Efficiency simulations - results a = 0.3, ν = a = 0.3, ν = σ σ Figure: R QO ( ), R DP ( ), R LEP ( ), and R URE ( ) Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

33 Conclusion Outline 1 Introduction 2 A posteriori parameter choice methods 3 Error analysis 4 Simulations 5 Conclusion Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34

34 Conclusion Presented results Analysis of a parameter choice based on unbiased risk estimation: oracle inequality convergence rates order optimality in mildly ill-posed situations Numerical comparison: in this specific setting, quasi-optimality outperforms all other methods unbiased risk estimation has higher variance (by design) simulations suggest order optimality of quasi-optimality also in severely ill-posed situations, not clear for unbiased risk estimation Thank you for your attention! Frank Werner, MPIbpC Göttingen Unbiased Risk Estimation October 30, / 34