Karhunen-Loève decomposition of Gaussian measures on...

Karhunen-Loeve decomposition of Gaussianmeasures on Banach spaces

Jean-Charles Croixjean-charles.croix@emse.fr

Genie Mathematique et Industriel (GMI)

First workshop on Gaussian processes at Saint-Etienne - November2017, 7th.

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Contents

1 Motivation

2 Karhunen-Loeve decomposition in Hilbert spacesGaussian vectors in Rn

Gaussian elements in Hilbert spaces

3 Karhunen-Loeve decomposition in Banach spaces

4 Particular case of (Rn, ‖.‖∞)

5 Particular case of C (K ), K metric and compact.Continuous Gaussian processesIllustration on Brownian motionSummability of λ

6 Conclusion and open questions

7 References

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Motivation

3 / 42

Motivation

The problem of representation of Gaussian elements in linear series isused in:

1 Simulation (e.g. truncated Karhunen-Loeve series),

2 Approximation and Dimension reduction (e.g. PCA or POD),

3 Optimal quantization,

4 Bayesian inverse problems.

If the existence of an optimal basis is well known in Hilbert spaces...

... it’s not always explicit (eigenvalue problem),

... it’s not the case in Banach spaces.

What if we are interested in non-Hilbertian norms ?

4 / 42

Motivation

In the case of a continuous Gaussian process on [0, 1] we may inject it inL2([0, 1], dx) and use Hilbertian geometry...

0.0 0.2 0.4 0.6 0.8 1.0−1.5

−1.0

−0.5

1.5Basis functions

f0f1f2f3f4

Figure: Karhunen-Loeve basis of Brownian motion in L2([0, 1], dx).

5 / 42

Motivation

or tackle the problem directly in C ([0, 1])...

0.0 0.2 0.4 0.6 0.8 1.0−0.2

1.2Basis functions

f0f1f2f3f4f5f6f7

Figure: Brownian motion basis functions in C([0, 1]) (Paul Levy’s construction).

...in the hope of better approximation w.r.t. supremum norm !6 / 42

Karhunen-Loeve decomposition in Hilbert spaces

7 / 42

Gaussian vector in Euclidian spaces

Consider the euclidian space Rn with canonical inner product 〈., .〉.

Gaussian random vector

Let (Ω,F ,P) a probability space and X : (Ω,F ,P)→ (Rn,B(Rn)) ameasurable mapping, then X is a Gaussian vector if and only if ∀y ∈ Rn,〈X , y〉 is a Gaussian random variable.

Covariance

Given a (centered) Gaussian random vector X , define the bilinear form:

∀x , y ∈ Rn, Cov(x , y) = E[〈X , x〉〈X , y〉], (1)

which uniquely defines Σ : Rn → Rn such that:

∀x , y ∈ Rn, 〈Σx , y〉 = Cov(x , y). (2)

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Gaussian vector in Euclidian spaces

Consider the euclidian space Rn with canonical inner product 〈., .〉.

Gaussian random vector

Let (Ω,F ,P) a probability space and X : (Ω,F ,P)→ (Rn,B(Rn)) ameasurable mapping, then X is a Gaussian vector if and only if ∀y ∈ Rn,〈X , y〉 is a Gaussian random variable.

Covariance

Given a (centered) Gaussian random vector X , define the bilinear form:

∀x , y ∈ Rn, Cov(x , y) = E[〈X , x〉〈X , y〉], (1)

which uniquely defines Σ : Rn → Rn such that:

∀x , y ∈ Rn, 〈Σx , y〉 = Cov(x , y). (2)

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Cameron-Martin and Gaussian spaces

Considering the Gaussian vector X gives a fundamental structure:

Gaussian space: Vect(〈X , ek〉, k ∈ [1, n]) ⊂ L2(P)

Cameron-Martin space HX = Range(Σ) equipped with〈., .〉X = 〈Σ−1., .〉

Loeve isometry

The following application:

〈X , x〉 ∈ L2(P)→ Σx ∈ HX (3)

is an isometry.

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Representation of Gaussian vectors in Rn

For any orthonormal basis (ek) in Rn, we can write:

X (ω) =n∑

〈X (ω), ek〉ek a.s. (4)

〈X (ω), ei 〉, 〈X (ω), ej〉 are independent if and only ifCov(ei , ej) = 〈Σei , ej〉 = 〈Σei ,Σej〉X = 0.For any orthonormal basis (xk) in HX :

X (ω) =

dim(HX )∑k=1

〈X (ω), xk〉X xk (5)

The Spectral theorem (on covariance operator) exhibits a particular(Karhunen-Loeve) basis (bi-orthogonal and Σhk = λkhk):

X (ω) =

dim(HX )∑k=1

√λk〈X (ω), hk〉hk (6)

Note Pk the linear projector on the k-th biggest eigenvalues, then:

∀k ≤ n, minrank(P)=k

E[‖X −PX‖2] = E[‖X −PkX‖2] = λk+1 + ...+λdim(HX )

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X (ω) =n∑

〈X (ω), ek〉ek a.s. (4)

〈X (ω), ei 〉, 〈X (ω), ej〉 are independent if and only ifCov(ei , ej) = 〈Σei , ej〉 = 〈Σei ,Σej〉X = 0.

For any orthonormal basis (xk) in HX :

X (ω) =

dim(HX )∑k=1

〈X (ω), xk〉X xk (5)

X (ω) =

dim(HX )∑k=1

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X (ω) =n∑

〈X (ω), ek〉ek a.s. (4)

X (ω) =

dim(HX )∑k=1

〈X (ω), xk〉X xk (5)

X (ω) =

dim(HX )∑k=1

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X (ω) =n∑

〈X (ω), ek〉ek a.s. (4)

X (ω) =

dim(HX )∑k=1

〈X (ω), xk〉X xk (5)

X (ω) =

dim(HX )∑k=1

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X (ω) =n∑

〈X (ω), ek〉ek a.s. (4)

X (ω) =

dim(HX )∑k=1

〈X (ω), xk〉X xk (5)

X (ω) =

dim(HX )∑k=1

(7)10 / 42

BE (0, 1)

√λ1

√λ2

BHX(0, 1)

Figure: Karhunen-Loeve basis in dimension 2.

11 / 42

Gaussian element in Hilbert spaces

Consider the (real) Hilbert space H with inner product 〈., .〉.

Gaussian random element

Let (Ω,F ,P) a probability space and X : (Ω,F ,P)→ (H,B(H)) ameasurable mapping, then X is a Gaussian element if and only if ∀y ∈ H,〈X , y〉 is a Gaussian random variable.

Covariance

Given a Gaussian random element X , define the bilinear form:

∀x , y ∈ H, Cov(x , y) = E[〈X , x〉〈X , y〉], (8)

and the associated covariance operator C : H → H such that:

∀x , y ∈ H, 〈Cx , y〉 = Cov(x , y). (9)

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Representation of Gaussian elements in Hilbert spaces

The covariance operator C : H → H is positive, symmetric andtrace-class (see Vakhania 1987).

The Cameron-Martin space HX is the completion of Range(C) w.r.t.〈x , y〉C = 〈C−1x , y〉 (it’s a proper subspace of H, HX → H, seeVakhania 1987).For any Hilbert basis (ek) in H we can write:

X (ω) =∑k≥0

〈X (ω), ek〉ek a.s. (10)

For any basis (xk) in HX and (ξk) i.i.d. N (0, 1):

X (ω)d=

∑k≥0

ξk(ω)xk (11)

Spectral theorem applies and exhibits a (Karhunen-Loeve)bi-orthogonal basis (hk): X (ω) =

∑k≥0

√λkξk(ω)hk a.s.

Eckart-Young theorem is still valid (functional PCA, ...)

∀k > 0, minrank(P)=k

E[‖X − PX‖2H ] = E[‖X − PkX‖2

H ] =∑i>k

λi (12)

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The covariance operator C : H → H is positive, symmetric andtrace-class (see Vakhania 1987).The Cameron-Martin space HX is the completion of Range(C) w.r.t.〈x , y〉C = 〈C−1x , y〉 (it’s a proper subspace of H, HX → H, seeVakhania 1987).

For any Hilbert basis (ek) in H we can write:

X (ω) =∑k≥0

〈X (ω), ek〉ek a.s. (10)

X (ω)d=

∑k≥0

ξk(ω)xk (11)

∑k≥0

E[‖X − PX‖2H ] = E[‖X − PkX‖2

H ] =∑i>k

λi (12)

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The covariance operator C : H → H is positive, symmetric andtrace-class (see Vakhania 1987).The Cameron-Martin space HX is the completion of Range(C) w.r.t.〈x , y〉C = 〈C−1x , y〉 (it’s a proper subspace of H, HX → H, seeVakhania 1987).For any Hilbert basis (ek) in H we can write:

X (ω) =∑k≥0

〈X (ω), ek〉ek a.s. (10)

X (ω)d=

∑k≥0

ξk(ω)xk (11)

∑k≥0

E[‖X − PX‖2H ] = E[‖X − PkX‖2

H ] =∑i>k

λi (12)

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X (ω) =∑k≥0

〈X (ω), ek〉ek a.s. (10)

X (ω)d=

∑k≥0

ξk(ω)xk (11)

∑k≥0

E[‖X − PX‖2H ] = E[‖X − PkX‖2

H ] =∑i>k

λi (12)

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X (ω) =∑k≥0

〈X (ω), ek〉ek a.s. (10)

X (ω)d=

∑k≥0

ξk(ω)xk (11)

∑k≥0

E[‖X − PX‖2H ] = E[‖X − PkX‖2

H ] =∑i>k

λi (12)

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X (ω) =∑k≥0

〈X (ω), ek〉ek a.s. (10)

X (ω)d=

∑k≥0

ξk(ω)xk (11)

∑k≥0

E[‖X − PX‖2H ] = E[‖X − PkX‖2

H ] =∑i>k

λi (12)

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Karhunen-Loeve decomposition in Banach spaces

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Gaussian element in Banach spaces

Consider the (real) Banach space E with duality pairing 〈., .〉E ,E∗ .

Gaussian random element

Let (Ω,F ,P) a probability space and X : (Ω,F ,P)→ (E ,B(E )) ameasurable mapping, then X is a Gaussian element if and only if∀f ∈ E∗, 〈X , f 〉E ,E∗ is a Gaussian random variable.

Covariance

Given a Gaussian random element X , define the bilinear form:

∀f , g ∈ E∗, Cov(f , g) = E[〈X , f 〉E ,E∗〈X , g〉E ,E∗ ], (13)

and the associated covariance operator C : E∗ → E (see Vakhania 1987or Bogachev 1998) such that:

∀f , g ∈ E∗, 〈Cf , g〉E ,E∗ = Cov(f , g). (14)

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Representation of Gaussian elements in Banach spaces

The covariance operator C : E∗ → E is positive, symmetric andnuclear (see Vakhania 1987).

The space HX is the completion of Range(C) w.r.t.〈x , y〉X = 〈y , C−1x〉E ,E∗ (it’s a proper subspace of E , HX → E , seeVakhania 1987, Bogachev 1998).

For any Hilbert basis (xk) in HX and (ξk) i.i.d. N (0, 1), we have:

∑k≥1

ξkxk (15)

C : E∗ → E ⇒ No Spectral theorem.

We know that (Bogachev 1998, Vakahnia 1991):∃(xk) ∈ Range(C) Hilbert basis in HX ,∃(xk) ∈ HX such that

∑k≥0‖hk‖

2E < +∞,

If the basis (xk) is in Range(C) with xk = Cx∗k , then:

X (ω) =∑k≥0

〈X (ω), x∗k 〉E ,E∗xk (16)

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∑k≥1

ξkxk (15)

∑k≥0‖hk‖

2E < +∞,

X (ω) =∑k≥0

〈X (ω), x∗k 〉E ,E∗xk (16)

16 / 42

∑k≥1

ξkxk (15)

∑k≥0‖hk‖

2E < +∞,

X (ω) =∑k≥0

〈X (ω), x∗k 〉E ,E∗xk (16)

16 / 42

∑k≥1

ξkxk (15)

∑k≥0‖hk‖

2E < +∞,

X (ω) =∑k≥0

〈X (ω), x∗k 〉E ,E∗xk (16)

16 / 42

∑k≥1

ξkxk (15)

∑k≥0‖hk‖

2E < +∞,

X (ω) =∑k≥0

〈X (ω), x∗k 〉E ,E∗xk (16)

16 / 42

∑k≥1

ξkxk (15)

∑k≥0‖hk‖

2E < +∞,

X (ω) =∑k≥0

〈X (ω), x∗k 〉E ,E∗xk (16)

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Factorization of covariance operators (Vakhania 1987, Bogachev 1998).

Let C be the covariance operator of a Gaussian element, then:

C = SS∗, (17)

where S : H → E is a bounded operator and H a Hilbert space.

Examples:In the Hilbert case, S = C 1

2 ,S : HX → E the inclusion map,

S : f ∈ E∗L2(P) → E[f (X )X ] ∈ E (S∗ is the injection from E∗ to L2)

(Luschgy and Pages 2009)

Let C = SS∗ with S : H → E . Then for any basis (hn) in H:

∑k≥0

ξkShk , (18)

where (ξk) are i.i.d. N (0, 1).

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C = SS∗, (17)

∑k≥0

ξkShk , (18)

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C = SS∗, (17)

∑k≥0

ξkShk , (18)

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This methodology has been widely used:

When E = C([0, 1]), S∗ : f ∈ E∗ → L2([0, 1], dx)...

We will now give a different methodology, mimicking the Hilbert case, toconstruct a Hilbert basis in HX . We will proceed as follows:

1 Find ”directions” of maximum variance,

2 Choose a right notion of orthogonality to iterate,

3 Study the asymptotics.

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Decomposition of the Cameron-Martin space

1 For any (Gaussian) covariance operator, f ∈ E∗ → Cov(f , f ) ∈ R+

may be interpreted as a Rayleigh quotient,is weakly sequentially continuous,is quadratic.

2 ∃f0 ∈ BE∗(0, 1) (non-unique) such thatCov(f0, f0) = max‖f ‖E∗≤1 Cov(f , f ) = λ0 (Banach-Alaoglu),

3 If λ0 > 0, let x0 ∈ E such that Cf0 = λ0x0, thenP0 : x ∈ E → 〈x , f0〉E ,E∗x0 is a projector of unit norm.

4 X = (P0X , (I − P0)X ).

5 Iterate on X1 = (I − P0)X .

Bay & Croix 2017

(λn) is non-increasing, λn → 0 and (√λkxk) is a Hilbert basis in HX .

We will note x∗0 = f0 and x∗n = fn −∑n−1

k=0〈xk , fn〉E ,E∗x∗k such thatPn =

∑nk=0 x

∗k ⊗ xk (similar to Gram-Schmidt).

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4 X = (P0X , (I − P0)X ).

Bay & Croix 2017

∑nk=0 x

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4 X = (P0X , (I − P0)X ).

Bay & Croix 2017

∑nk=0 x

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4 X = (P0X , (I − P0)X ).

Bay & Croix 2017

∑nk=0 x

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4 X = (P0X , (I − P0)X ).

Bay & Croix 2017

∑nk=0 x

19 / 42

4 X = (P0X , (I − P0)X ).

Bay & Croix 2017

∑nk=0 x

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4 X = (P0X , (I − P0)X ).

Bay & Croix 2017

∑nk=0 x

19 / 42

Properties of the decomposition

Few comments about the previous construction...

Properties

This construction gives the following properties:

X (ω) =∑

k≥0〈X (ω), x∗k 〉E ,E∗xk a.s.

C = SS∗ with S∗ : f ∈ E∗ →∑

k≥0 λk〈xk , f 〉E ,E∗xk

Note Cn =∑n

k=0 λkxk ⊗ xk then ‖C − Cn‖L(E∗,E) = λn+1.

Recovers Karhunen-Loeve basis in the Hilbert case.

(xk , x∗k ) ⊂ E × E∗ is bi-orthogonal.

Vect(xk , k ≥ 0)E

∀k ∈ N, ‖fk‖E∗ = ‖xk‖E = 1.

Remarks∑k≥0 λk need not be finite (not a nuclear representation of C).

Optimality seems out of reach (Rate optimality ?).

20 / 42

Particular case of (Rn, ‖.‖∞)

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Application in Rn

Here, E∗ = (Rn, ‖.‖1) and ∀(x , f ) ∈ E × E∗, 〈x , f 〉E ,E∗ =∑n

i=1 xi fi .

Suppose a centered dataset (yi )i∈[1,p] ∈ (Rn)p,

Form the usual covariance matrix Σ ∈Mn(R),

Find the direction of maximal variance f0 ∈ Rn:

λ0 = f T0 Σf0 = maxi∈[1,n]

Σ(i,i) with f0 = (0, ..., 0, 1, 0, ..., 0) (19)

If λ0 > 0 then x0 = Σf0λ0

Σ1 = Σ− λ0x0xT0

Iterate.

22 / 42

Application in Rn

Residuals norm as rk = 1n

∑ni=1‖xi −

∑kj=0〈xi , x∗j 〉E ,E∗xj‖∞,

Projections norm as pk = 1n

∑ni=1‖

∑kj=0〈xi , x∗j 〉E ,E∗xj‖∞.

Figure: Residuals (decreasing) and projections (increasing) norm (blue forL2-PCA, red for Banach PCA)

23 / 42

Particular case of C (K ), K metric and compact.

24 / 42

E = C (K ) with K metric compact.

Let X be a Gaussian element such that X ∈ C (K ) almost-surely andsuppose that

k : (s, t) ∈ K 2 → 〈Cδs , δt〉E ,E∗ = Cov(Xs ,Xt) ∈ R (20)

is continuous. The Cameron-Martin space HX coincides here with theReproducing Kernel Hilbert space (RKHS). The previous decompositionbecomes:

1 Set n = 0 and kn = k ,

2 Find xn ∈ K such that kn(xn, xn) = maxx∈K kn(x , x),

3 If λn = kn(xn, xn) > 0 then kn+1(s, t) = kn(s, t)− kn(s,xn)kn(xn,t)λn

4 n← n + 1.

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Illustration on Brownian motion: Step 1

Figure: Left: Brownian motion samples and point-wise (symmetrized) variance.Right: First basis function x0(t) = t associated to f0 = δ1.

26 / 42

Figure: Left: Brownian bridge samples and point-wise (symmetrized) variance.Right: Second basis function x1(t) = min(t, 0.5)− 0.5t, associated to f1 = δ 1

27 / 42

Figure: Left: Conditional Brownian motion samples and point-wise(symmetrized) variance. Right: Third and fourth basis functions.

28 / 42

A word on summability

The proposed decomposition need not be a nuclear representation of C,that is

∑k≥0 λk ∈ [0,+∞]. Indeed, the Brownian case shows us:∑k≥0

λk = 1 +1

2+ 2 ∗ 1

4+ 4 ∗ 1

16+ 8 ∗ 1

32+ ... = +∞ (21)

However, a simple transformation gives a nuclear representation in thiscase:

f 12 =

f 12 − f 2

2⇒ Cov(f 1

2 , f1

2 ) =Cov(f 1

2 , f1

f 22 =

f 12 + f 2

2⇒ Cov(f 2

2 , f2

2 ) =Cov(f 2

2 , f2

Doing similar transformations at each step gives:∑k≥0

λk = 1 +1

2+ 2 ∗ 1

8+ 4 ∗ 1

64+ 8 ∗ 1

256+ ... = 2 (24)

Remark that E[‖∑

k≥n ξkhk‖2] ≤∑

k≥n λk , thus the approximation erroris exponentially decreasing !

29 / 42

Conclusion and open questions

30 / 42

Conclusion and open questions

Conclusions

New ”practical” method to represent Gaussian elements, based oncovariance operator.

Numerical solution in the case of E = C (K ) (K metric andcompact).

Rediscovers Paul Levy’s construction of Brownian motion.

Open question & future work

Other properties of (xk , x∗k ) ∈ E × E∗ ?

Rate optimality of finite dimensional approximations in C (K )?

Multiplicity of decomposition elements ?

Links with approximation numbers (ex: l(X ) = E[‖X‖2]12 )

approximation theory (s-numbers) ? Properties of Banach spaces ?

Optimality for projection norm ? Dual problem ?

Interpretation of∑

k≥0 λk when it is finite ?

Evolution of ‖x∗k ‖E∗ ?

31 / 42

References

32 / 42

References

X. Bay, J.-C. Croix.Karhunen-Loeve decomposition of Gaussian measures on Banachspaces.ArXiV, 2017.

H. Luschgy, G. Pages.Expansions For Gaussian Processes And Parseval Frames.Electronic Journal of Probability, 14, 2009, 1198-1221.

N. Vakhania.Canonical Factorization of Gaussian covariance operators and someof its applications.Theory of Probability and Its Applications, 1991.

V. Bogachev.Gaussian Measures.Mathematical Surveys and Monographs vol. 62, AMS, 1998.

N. Vakhania, V. Tarieladze, S. Chobanyan.Probability Distributions on Banach spaces.Mathematics and Its Applications, Springer Netherlands, 1987.

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Appendix

34 / 42

Examples with different kernels

0.0 0.2 0.4 0.6 0.8 1.0−0.2

1.2Basis functions

f0f1f2f3f4f5f6f7

Figure: Wiener measure basis functions.

35 / 42

0 2 4 6 8 1010-2

101 Variances

Variances

Figure: Wiener measure basis variances.

36 / 42

0.0 0.2 0.4 0.6 0.8 1.0−1.0

−0.5

1.5Basis functions

f0f1f2f3f4f5f6f7

Figure: k(s, t) = exp(− (s−t)2

)basis functions.

37 / 42

0 1 2 3 4 5 6 7 810-13

101 Variances

Variances

Figure: k(s, t) = exp(− (s−t)2

)basis variances.

38 / 42

0.0 0.2 0.4 0.6 0.8 1.0

−0.5

Basis functions

x0x1x2x3x4x5x6x7x8x9x10x11

Figure: Matern 3/2 kernel basis functions.

39 / 42

0 2 4 6 8 1010-4

101 Variances

Variances

Figure: Matern 3/2 kernel basis variances.

40 / 42

0.0 0.2 0.4 0.6 0.8 1.0−0.2

1.2Basis functions

f0f1f2f3f4f5f6f7

Figure: Fractional brownian motion (H = 75%) basis functions.

41 / 42

0.0 0.2 0.4 0.6 0.8 1.0−0.2

1.2Basis functions

f0f1f2f3f4f5f6f7

Figure: Fractional brownian motion (H = 25%) basis functions.

42 / 42

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4.4. Gaussian Elimination for Sparse Matrices€¦ · 3.3 QR-Decomposition with Householder matrices 4 Sparse Matrices 4.1 General Properties, Storage 4.2 Sparse Matrices and Graphs

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