Esa Ollila

(Aalto University, Finland)

https://s3-seminar.github.io/seminars/esa-ollila

Title — Robust approaches to multichannel sparse recovery

Abstract — We consider multichannel sparse recovery problem where the objective is to find good recovery of jointly sparse unknown signal vectors from the given multiple measurement vectors which are different linear combinations of the same known elementary vectors (atoms). The model is thus an extension of single measurement vector setting used in compressed sensing (CS). Many popular greedy or convex algorithms proposed for multichannel sparse recovery problem perform poorly under non-Gaussian heavy-tailed noise conditions or in the face of outliers (gross errors), i.e., are not robust. In this talk, we consider different types of mixed robust norms on data fidelity (residual matrix) term and conventional L0-norm constraint on the signal matrix to promote row-sparsity. We devise algorithms based normalized iterative hard thresholding (blumesath and davies, 2010) which is a simple, computationally efficient and scalable approach for solving the simultaneous sparse approximation problem. Performance assessment conducted on simulated data highlights the effectiveness of the proposed approaches to cope with different noise environments (i.i.d., row i.i.d, etc) and outliers. Usefulness of the methods are illustrated in image denoising problem and source localization application with sensor arrays. Finally (if time permits) a (non-robust) Bayesian perspective to multichannel recovery problem is discussed as well.

Biography — Esa Ollila received the M.Sc. degree in mathematics from the University of Oulu, in 1998, Ph.D. degree in statistics with honors from the University of yvaskyla, in 2002, and the D.Sc.(Tech) degree with honors in signal processing from Aalto University, in 2010. From 2004 to 2007 he was a post-doctoral fellow of the Academy of Finland. He has also been a Senior Researcher and a Senior Lecturer at Aalto University and University of Oulu, respectively. Currently, from August 2010, he is appointed as an Academy Research Fellow of the Academy of Finland at the Department of Signal Processing and Acoustics, Aalto University, Finland. He is also adjunct Professor (statistics) of University of Oulu. During the Fall-term 2001 he was a Visiting Researcher with the Department of Statistics, Pennsylvania State University, State College, PA while the academic year 2010-2011 he spent as a Visiting Research Associate with the Department of Electrical Engineering, Princeton University, Princeton, NJ. His research interests focus on theory and methods of statistical signal processing, blind source separation, complex-valued signal processing, array and radar signal processing and robust and non-parametric statistical methods.

Transcript

1. Robust multichannel sparse
   recovery
   Esa Ollila
   Department of Signal Processing and Acoustics
   Aalto University, Finland
   SUPELEC, Feb 4th, 2015
   

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2. 1 Introduction
   2 Nonparametric sparse recovery
   3 Simulation studies
   4 Source localization with Sensor Arrays
   

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3. Single Measurement Vector
   Model (SMV)
   y
   M × 1
   = Φ
   M × N
   x
   N × 1
   + e
   N × 1
   Φ = φ1
   · · · φN
   = φ(1)
   · · · φ(M)
   H,
   M × N known measurement (design, system) matrix
   x = (x1, . . . , xN
   )⊤, unobserved signal vector
   e = (e1, . . . , eM
   )⊤, random noise vector.
   Objective
   recover K-sparse or compressible signal from y knowing Φ, K
   NOTE: typically, M < N (ill-posed model) and K ≪ N.
   2/24
   

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4. Single Measurement Vector
   Model (SMV)
   y
   M × 1
   = Φ
   M × N
   x
   N × 1
   + e
   M × 1
   If we can determine Γ = supp(x), then the problem reduces to
   conventional regression problem (more ’responses’ than ’predictors’):
   y Φ x e
   3/24
   

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5. Multiple Measurement Vectors
   Model (MMV)
   yi
   = Φxi
   + ei , i = 1, . . . , Q
   In matrix form
   Y
   M × Q
   = Φ
   M × N
   X
   N × Q
   + E
   M × 1
   Y = y1
   · · · yQ
   , observed measurement matrix
   X = x1
   · · · xQ
   , unobserved signal matrix
   E = e1
   · · · eQ
   , noise matrix
   Objective
   recover K-sparse (or compressible) signal matrix X knowing Y, Φ, K.
   N × Q matrix X is called K-sparse if X 0
   = |supp(X)| ≤ K, where
   Γ = supp(X) =
   Q
   i=1
   supp(xi
   ) = {j ∈ {1, . . . , N} : xjk
   = 0 for some k}
   4/24
   

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6. Multiple Measurement Vectors
   Model (MMV)
   Key idea:
   signals are all predominantly supported on a common support set
   =⇒ Joint estimation can lead both to computational advantages
   and increased reconstruction accuracy
   See [Tropp, 2006, Chen and Huo, 2006, Eldar and Rauhut, 2010,
   Duarte and Eldar, 2011, Blanchard et al., 2014].
   Applications
   EEG/MEG [Gorodnitsky et al., 1995]
   equalization of sparse communications channels [Cotter and Rao, 2002]
   blind source separation [Gribonval and Zibulevsky, 2010]
   source localization using sensor arrays [Malioutov et al., 2005]
   . . .
   5/24
   

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7. Matrix norms
   The mixed ℓp,q
   norm of X for p, q ∈ [1, ∞)
   X p,q
   =
   i j
   |xij
   |p
   q/p 1/q
   =
   i
   x(i)
   q
   p
   1/q
   .
   If p = q =⇒ matrix p-norm X p,p
   = X p
   .
   ℓ2
   (Frobenius) norm · 2
   is denoted shortly as · .
   The mixed ℓ
   ∞
   -norms of X ∈ CN×Q
   X p,∞
   = maxi∈{1,...,N} x(i) p
   X
   ∞,q
   =
   i
   (maxj∈{1,...,Q}
   |xij
   |)q 1/q.
   The row ℓ0
   quasi-norm of X
   X 0
   = | supp(X)| = nr. of number of nonzero rows of X
   6/24
   

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8. Optimization problem
   min
   X
   Y − ΦX 2 subject to X 0
   ≤ K
   is combinatorial (NP-hard). More feasible approaches:
   Optimization (Convex relaxation)
   Greedy pursuit:
   Orthogonal Matching Pursuit (OMP)
   Normalized Iterative Hard Thresholding (NIHT)
   Normalized Hard Thresholding Pursuit (NHTP)
   Compressive Sampling MP (CoSaMP)
   Guaranteed to perform very well under suitable conditions (restricted
   isometry property on Φ, non impulsive noise e)
   NIHT is simple and scalable, convenient for problems where the
   support is of primary interest
   7/24
   

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9. Optimization problem
   min
   X
   Y − ΦX 2 subject to X 0
   ≤ K
   is combinatorial (NP-hard). More feasible approaches:
   Optimization (Convex relaxation)
   Greedy pursuit:
   Orthogonal Matching Pursuit (OMP)
   Normalized Iterative Hard Thresholding (NIHT)
   Normalized Hard Thresholding Pursuit (NHTP)
   Compressive Sampling MP (CoSaMP)
   Guaranteed to perform very well under suitable conditions (restricted
   isometry property on Φ, non impulsive noise e)
   NIHT is simple and scalable, convenient for problems where the
   support is of primary interest
   7/24
   

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10. Convex relaxation
    IDEA: replace X 0
    by ℓp,1
    norm
    [Tropp, 2006] uses p = ∞:
    min
    X
    Y − ΦX 2 + λ X
    ∞,1
    where X
    ∞,1
    =
    i
    |x(i) ∞
    .
    [Malioutov et al., 2005] uses p = 2:
    min
    X
    Y − ΦX 2 + λ X 2,1
    where X 2,1
    =
    i
    |x(i)
    .
    good recovery depends on the choise of egularization parameter λ > 0
    not robust due to the used data fidelity term (LS-loss)
    not as scalable as greedy methods
    8/24
    

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11. 1 Introduction
    2 Nonparametric sparse recovery
    3 Simulation studies
    4 Source localization with Sensor Arrays
    

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12. Nonparametric approach based
    on mixed norms
    min
    X
    Y − ΦX q
    p,q
    data fidelity
    subject to X 0
    ≤ K
    sparsity constraint
    (Pp,q
    )
    =⇒ mixed ℓ1
    norms provide robustness, i.e., give larger weights on
    small residuals and less weight on large residuals.
    We consider the cases
    (p, q) = (1, 1): Y − ΦX 1
    =
    i j
    |yij
    − φH
    (i)
    xj
    |
    robust norm for errors i.i.d. in space and time
    (p, q) = (2, 1): Y − ΦX 2,1
    =
    i
    y(i)
    − XHφ(i)
    robust norm for errors i.i.d. in ”space” only
    and devise greedy simultaneous NIHT (SNIHT) algorithm
    9/24
    

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13. Normalized iterative hard
    thresholding (NIHT)
    Conventional ℓ2,2
    approach :
    Xn+1 = HK
    ↑
    hard thresholding
    Xn +
    stepsize
    µn+1 ΦH(Y − ΦXn)
    ↑
    neg. gradient ∇X∗
    · 2
    ψ2,2
    (X) = X =⇒ Conventional ℓ2,2
    approach
    ψ1,1
    (X) = [sign(xij
    )] (elementwise operation of S(·))
    ψ2,1
    (X) = [sign(x(i)
    )] (rowwise operation of S(·))
    10/24
    

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14. Normalized iterative hard
    thresholding (NIHT)
    In our mixed norm ℓp,q
    approach:
    Xn+1 = HK
    ↑
    hard thresholding
    Xn +
    stepsize
    µn+1 ΦHψp,q
    (Y − ΦXn)
    ↑
    neg. gradient ∇X∗
    · q
    p,q
    ψ2,2
    (X) = X =⇒ Conventional ℓ2,2
    approach
    ψ1,1
    (X) = [sign(xij
    )] (elementwise operation of S(·))
    ψ2,1
    (X) = [sign(x(i)
    )] (rowwise operation of S(·))
    10/24
    

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15. Normalized iterative hard
    thresholding (NIHT)
    In our mixed norm ℓp,q
    approach:
    Xn+1 = HK
    ↑
    hard thresholding
    Xn +
    stepsize
    µn+1 ΦHψp,q
    (Y − ΦXn)
    ↑
    neg. gradient ∇X∗
    · q
    p,q
    ψ2,2
    (X) = X =⇒ Conventional ℓ2,2
    approach
    ψ1,1
    (X) = [sign(xij
    )] (elementwise operation of S(·))
    ψ2,1
    (X) = [sign(x(i)
    )] (rowwise operation of S(·))
    Spatial sign
    sign(x) =
    x/ x , for x = 0
    0, for x = 0
    10/24
    

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16. Normalized iterative hard
    thresholding (NIHT)
    In our mixed norm ℓp,q
    approach:
    Xn+1 = HK
    ↑
    hard thresholding
    Xn +
    stepsize
    µn+1 ΦHψp,q
    (Y − ΦXn)
    ↑
    neg. gradient ∇X∗
    · q
    p,q
    ψ2,2
    (X) = X =⇒ Conventional ℓ2,2
    approach
    ψ1,1
    (X) = [sign(xij
    )] (elementwise operation of S(·))
    ψ2,1
    (X) = [sign(x(i)
    )] (rowwise operation of S(·))
    Spatial sign
    sign(x) =
    x/ x , for x = 0
    0, for x = 0
    10/24
    

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17. SNIHT(p, q) algorithm
    input : Y, Φ, sparsity K, mixed norm indices (p, q)
    output : (Xn+1, Γn+1) as estimates of X and Γ = supp(X)
    initialize: X0 = 0, µ0 = 0, n = 0, Γ0 = ∅
    1 Γ0 = supp HK
    (ΦHψp,q
    (Y))
    while halting criterion false do
    2 Rn
    ψ
    = ψp,q
    (Y − ΦXn)
    3 Gn = ΦHRn
    ψ
    4 µn+1 = CompStepsize(Φ, Gn, Γn, µn, p, q)
    5 Xn+1 = HK
    (Xn + µn+1Gn)
    6 Γn+1 = supp(Xn+1)
    7 n = n + 1
    end
    11/24
    

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18. Computation of the step size µn+1
    For convergence, it is important that the stepsize is adaptively
    controlled at each iteration.
    Given the found support Γn is correct, one can choose µn+1 as the
    min. of the objective fnc for the gradient ascent direction
    Xn + µGn|Γn
    :
    L(µ) = Y − Φ Xn + µGn|Γn
    q
    p,q
    = Rn − µB q
    p,q
    where Rn = Y − ΦXn and B = ΦG(Γn)
    For p = q: simple linear regression problem based on ℓp
    -norm, with
    response r = vec(Rn) and predictor b = vec(B).
    ⇒ µn+1 = Gn
    (Γn)
    2/ ΦGn
    (Γn)
    2 for p = q = 2
    For p = q = 1 and (p, q) = (2, 1), minimizer of L(µ) can not be
    found in closed-form.
    12/24
    

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19. Computation of the step size µn+1
    When p = q = 1, the solution µ verifies a fixed point (FP) equation
    µ = H(µ)
    H(µ) =
    i,j
    |˜
    rij
    |−1|bij
    |2
    −1
    i,j
    |˜
    rij
    |−1Re(b∗
    ij
    rij
    ),
    and ˜
    R = Rn − µB (depends on unknown µ) and B = ΦG(Γn)
    .
    We select µn+1 as the 1-step FP iterate
    µn+1 = H(µn),
    where µn is the value of the stepsize at previous nth iteration.
    Often this approximation was within ∼ 1e−3 to minimizer of L(µ).
    Same approach is used in the case (p, q) = (2, 1).
    13/24
    

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20. 1 Introduction
    2 Nonparametric sparse recovery
    3 Simulation studies
    4 Source localization with Sensor Arrays
    

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21. Simulation study
    Y
    M × Q
    = Φ
    M × N
    X
    N × Q
    + E
    M × 1
    Set-up
    Measurement matrix Φ: φij
    ∼ CN(0, 1) with unit-norm columns
    Γ = supp(X) randomly drawn K elements from {1, . . . , N}
    signal matrix X: |xij
    | = 10 and Arg(xij
    ) ∼ Unif (0, 2π) ∀i ∈ Γ.
    For ℓ = 1, . . . , L MC-trials (L = 1000), compute X[ℓ] and Γ[ℓ]
    Performance measures
    mean squared error MSE( ^
    X) = 1
    LQ
    L
    ℓ=1
    ^
    X[ℓ] − X[ℓ] 2.
    probability of exact recovery PER =
    1
    L
    L
    ℓ=1
    I ^
    Γ[ℓ] = Γ[ℓ] .
    14/24
    

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22. IIID Gaussian noise, eij
    ∼ CN (0, σ2)
    MSE vs SNR for Q=16
    0 4 8 12 16 20
    10
    15
    20
    25
    30
    35
    SNR (dB)
    MSE (dB)
    SNIHT(2, 2)
    SNIHT(2, 1)
    SNIHT(1, 1)
    HUB-SNIHT
    PER vs Q at SNR = 5 dB
    2 4 6 8 10 12 14 16 18
    0
    0.2
    0.4
    0.6
    0.8
    1
    Q (number of vectors)
    PER
    SNIHT(2, 2)
    SNIHT(2, 1)
    SNIHT(1, 1)
    HUB-SNIHT
    SNIHT(2, 2)
    but SNIHT(2, 1) and HUB-SNIHT (threshold
    c = 1.517) loose only 0.09 dB in MSE (Q = 16)
    15/24
    

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23. IID t-distributed noise, eij
    ∼ Ctν
    (0, σ2),
    σ2 = MedF
    (|ei
    |2)
    SNR(σ) = 30 dB
    1 1.5 2 3 4 5
    0
    10
    20
    30
    40
    50
    60
    ν (degrees of freedom)
    MSE (dB)
    SNIHT(2, 2)
    SNIHT(2, 1)
    SNIHT(1, 1)
    HUB-SNIHT
    SNR(σ) = 10 dB, ν = 3
    2 4 6 8 10 12 14 16 18
    0
    0.2
    0.4
    0.6
    0.8
    1
    Q (number of vectors)
    PER
    SNIHT(2, 2)
    SNIHT(2, 1)
    SNIHT(1, 1)
    HUB-SNIHT
    SNIHT(1, 1)
    16/24
    

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24. Row IIID compound Gaussian
    inverse Gaussian texture, e(i)
    ∼ CIGλ
    (0, σ2IQ
    )
    0 4 8 12 16 20
    0
    5
    10
    15
    20
    25
    30
    SNR (dB)
    MSE (dB)
    SNIHT(2, 2)
    SNIHT(2, 1)
    SNIHT(1, 1)
    HUB-SNIHT
    SNIHT(2, 1)
    17/24
    

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25. 1 Introduction
    2 Nonparametric sparse recovery
    3 Simulation studies
    4 Source localization with Sensor Arrays
    

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26. Sensor array and narrowband,
    farfield source model
    M sensors, K sources (M > K):
    Model for sensor measurements at time instant t
    y(t) = A(θ)x(t) + e(t),
    - steering matrix A(θ) = a(θ1
    ) . . . a(θK
    )
    - unknown (distinct) Direction of Arrivals (DOA’s) θ1, . . . , θK
    - known array manifold a(θ) (e.g., ULA)
    - unknown signal waveforms x(t) = (x1
    (t), . . . , xK
    (t))⊤
    Objective
    Estimate the DOA’s of the sources given the snapshots y(t1
    ), . . . , y(tQ
    )
    and the number of sources K impinging on the sensor array.
    18/24
    

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27. Multichannel sparse recovery
    approach to source localization
    Construct an M × N overcomplete steering matrix A(˜
    θ) for a grid of
    all source locations ˜
    θ1, . . . , ˜
    θN
    of interest.
    Suppose that ˜
    θ contains the true DOA’s to some accuracy.
    The array model in matrix form then rewrites as
    Y = y(t1
    ) · · · y(tQ
    ) = A(˜
    θ)X + E,
    where X ∈ CN×Q is K-sparse, with K non-zero rows containing the
    source waveforms at time instants t1, . . . , tQ
    .
    =⇒ K-sparse MMV model
    In the multichannel sparce recovery formulation A(˜
    θ) is completely
    known, and we can use SNIHT methods to identify the support.
    19/24
    

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28. Multichannel sparse recovery
    approach to source localization
    Construct an M × N overcomplete steering matrix A(˜
    θ) for a grid of
    all source locations ˜
    θ1, . . . , ˜
    θN
    of interest.
    Suppose that ˜
    θ contains the true DOA’s to some accuracy.
    The array model in matrix form then rewrites as
    Y = y(t1
    ) · · · y(tQ
    ) = A(˜
    θ)X + E,
    where X ∈ CN×Q is K-sparse, with K non-zero rows containing the
    source waveforms at time instants t1, . . . , tQ
    .
    =⇒ K-sparse MMV model
    In the multichannel sparce recovery formulation A(˜
    θ) is completely
    known, and we can use SNIHT methods to identify the support.
    19/24
    

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29. Multichannel sparse recovery
    approach to source localization
    Construct an M × N overcomplete steering matrix A(˜
    θ) for a grid of
    all source locations ˜
    θ1, . . . , ˜
    θN
    of interest.
    Suppose that ˜
    θ contains the true DOA’s to some accuracy.
    The array model in matrix form then rewrites as
    Y = y(t1
    ) · · · y(tQ
    ) = A(˜
    θ)X + E,
    where X ∈ CN×Q is K-sparse, with K non-zero rows containing the
    source waveforms at time instants t1, . . . , tQ
    .
    =⇒ Finding DOA’s is equivalent to identifying Γ = supp(X)
    In the multichannel sparce recovery formulation A(˜
    θ) is completely
    known, and we can use SNIHT methods to identify the support.
    19/24
    

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30. Multichannel sparse recovery
    approach to source localization
    Construct an M × N overcomplete steering matrix A(˜
    θ) for a grid of
    all source locations ˜
    θ1, . . . , ˜
    θN
    of interest.
    Suppose that ˜
    θ contains the true DOA’s to some accuracy.
    The array model in matrix form then rewrites as
    Y = y(t1
    ) · · · y(tQ
    ) = A(˜
    θ)X + E,
    where X ∈ CN×Q is K-sparse, with K non-zero rows containing the
    source waveforms at time instants t1, . . . , tQ
    .
    =⇒ Finding DOA’s is equivalent to identifying Γ = supp(X)
    In the multichannel sparce recovery formulation A(˜
    θ) is completely
    known, and we can use SNIHT methods to identify the support.
    19/24
    

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31. Simul set-up
    ULA of M = 20 sensors with half a wavelength interelement spacing.
    K = 2 independent Gaussian sources from θ1
    = 0o and θ2
    = 8o.
    Temporally/spatially white Gaussian error terms.
    Low SNR = 0 dB.
    Uniform grid ˜
    θ on [−90, 90] with 2o degree spacing
    We compute PER rates and relative frequencies of found DOA
    estimates in the grid for 1000 Monte Carlo runs.
    20/24
    

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32. Number of snapshots Q = 4
    DOA’s at θ1
    = 0o and θ1
    = 8o.
    −4 −2 0 2 4 6 8 10 12 14
    0
    0.2
    0.4
    0.6
    0.8
    1
    DOA θ degrees (2o sampling grid)
    Frequency
    SNIHT(2, 2)
    SNIHT(1, 1)
    SNIHT(2, 1)
    PER rates
    1 SNIHT(1, 1) = 81%
    2 SNIHT(2, 1) = 73.1%
    3 HUB-SNIHT = 42.6%
    4 SNIHT = 37.6%
    SNIHT(1, 1)
    21/24
    

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33. Number of snapshots Q = 40
    DOA’s at θ1
    = 0o and θ1
    = 8o.
    −4 −2 0 2 4 6 8 10 12 14
    0
    0.2
    0.4
    0.6
    0.8
    1
    DOA θ degrees (2o sampling grid)
    Frequency
    SNIHT(2, 2)
    SNIHT(1, 1)
    SNIHT(2, 1)
    PER rates
    1 SNIHT(1, 1) = 100%
    2 SNIHT(2, 1) = 73.1%
    3 HUB-SNIHT = 42.6%
    4 SNIHT = 37.6%
    SNIHT(1, 1)
    22/24
    

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34. Conclusions
    robust and nonparametric appraches for simultaneous sparse recovery
    23/24
    

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35. Conclusions
    robust and nonparametric appraches for simultaneous sparse recovery
    Different method, SNIHT(1, 1), SNIHT(2, 1), HUB-SNIHT, can be
    selected for a problem at hand (iid/correlated noise, high-breakdown, etc)
    23/24
    

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36. Conclusions
    robust and nonparametric appraches for simultaneous sparse recovery
    Different method, SNIHT(1, 1), SNIHT(2, 1), HUB-SNIHT, can be
    selected for a problem at hand (iid/correlated noise, high-breakdown, etc)
    Fast and scalable greedy SNIHT algorithm: increase in robustness does
    not imply increase in computation time!
    23/24
    

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37. Conclusions
    robust and nonparametric appraches for simultaneous sparse recovery
    Different method, SNIHT(1, 1), SNIHT(2, 1), HUB-SNIHT, can be
    selected for a problem at hand (iid/correlated noise, high-breakdown, etc)
    Fast and scalable greedy SNIHT algorithm: increase in robustness does
    not imply increase in computation time!
    Applications in source localization and image denoising, etc.
    23/24
    

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38. This talk based on
    E. Ollila (2015a)
    Nonparametric Simultaneous Sparse Recovery: an Application to
    Source Localization
    PDF (ArXiv): http://arxiv.org/pdf/1502.02441v1
    EUSIPCO’15, submitted.
    E. Ollila (2015b).
    Robust Simultaneous Sparse Approximation
    A Festschrift in Honor of Hannu Oja, Springer, Sept. 2015.
    24/24
    

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