Sparse Signal Recovery: Theory, Applications and Algorithms

Transcription

1 Sparse Signal Recovery: Theory, Applications and Algorithms Bhaskar Rao Department of Electrical and Computer Engineering University of California, San Diego Collaborators: I. Gorodonitsky, S. Cotter, D. Wipf, J. Palmer, K. Kreutz-Delgado Acknowledgement: Travel support provided by Office of Naval Research Global under Grant Number: N The research was supported by several grants from the National Science Foundation, most recently CCF / 42

2 Outline 1 Talk Objective 2 Sparse Signal Recovery Problem 3 Applications 4 Computational Algorithms 5 Performance Evaluation 6 Conclusion 2 / 42

3 Outline 1 Talk Objective 2 Sparse Signal Recovery Problem 3 Applications 4 Computational Algorithms 5 Performance Evaluation 6 Conclusion 3 / 42

4 Importance of Problem DSPl7: DSP EDUCATION VOLUME I11 DSP Instead of Circuits? - Transition to Undergraduate DSP Education at Rose-Hulman W. Padgett, M. Yoder (Rose-Hulman Institute of Tecnology, USA) Organized A with Java Signal Prof. Analysis Tool Bresler for Signal Processing a Special Experiments... Session at ~...,..,...,.,...,..,...,,...III-l~9 the 1998 IEEE A. Clausen, A. Spanias, A. Xavier, M. Tampi (Arizona State University, USA) International Visualization Conference of Signal Processing on Concepts Acoustics,... Speech and Signal Processing J. Shaffer, J. Hamaker, J. Picone (Mississippi State University, USA) An Experimental Architecture for Interactive Web-based DSP Education M. Rahkila, M. Karjalainen (Helsinki University of Technology, Finland) SPEC-DSP: SIGNAL PROCESSING WITH SPARSENESS CONSTRAINT Signal Processing with the Sparseness Constraint B. Rao (University of California, San Diego, USA) Application of Basis Pursuit in Spectrum Estimation S. Chen (IBM, USA); D. Donoho (Stanford University, USA) Parsimony and Wavelet Method for Denoising H. Krim (MIT, USA); J. Pesquet (University Paris Sud, France); I. Schick (GTE Ynternetworking and Harvard Univ., USA) Parsimonious Side Propagation P. Bradley, 0. Mangasarian (University of Wisconsin-Madison, USA) Fast Optimal and Suboptimal Algorithms for Sparse Solutions to Linear Inverse Problems G. Harikumar (Tellabs Research, USA); C. Couvreur, Y. Bresler (University of Illinois, Urbana-Champaign. USA) Measures and Algorithms for Best Basis Selection K. Kreutz-Delgado, B. Rao (University of Califomia, San Diego, USA) Sparse Inverse Solution Methods for Signal and Image Processing Applications B. Jeffs (Brigham Young University, USA) Image Denoising Using Multiple Compaction Domains P. Ishwar, K. Ratakonda, P. Moulin, N. Ahuja (University of Illinois, Urbana-Champaign, USA) SPEC-EDUCATION: FUTURE DIRECTIONS IN SIGNAL PROCESSING EDUCATION A Different First Course in Electrical Engineering D. Johnson, J. Wise, Jr. (Rice University, USA) 4 / 42

5 Talk Goals Sparse Signal Recovery is an interesting area with many potential applications Tools developed for solving the Sparse Signal Recovery problem are useful for signal processing practitioners to know 5 / 42

6 Outline 1 Talk Objective 2 Sparse Signal Recovery Problem 3 Applications 4 Computational Algorithms 5 Performance Evaluation 6 Conclusion 6 / 42

7 Problem Description t is N 1measurementvector ΦisN M Dictionary matrix. M >> N. w is M 1desiredvectorwhichissparsewithK non-zero entries is the additive noise modeled as additive white Gaussian 7 / 42

8 Problem Statement Noise Free Case: Givenatargetsignalt and a dictionary Φ, find the weights w that solve: min w M I (w i =0) suchthat t =Φw i=1 where I (.) istheindicatorfunction Noisy Case: Givenatargetsignalt and a dictionary Φ, find the weights w that solve: min w M I (w i =0) suchthat t Φw 2 2 β i=1 8 / 42

9 Complexity Search over all possible subsets, which would mean a search over atotalof( M C K ) subsets. Problem NP hard. With M =30, N =20, and K =10thereare subsets (Very Complex) Abranchandboundalgorithmcanbeusedtofindtheoptimal solution. The space of subsets searched is pruned but the search may still be very complex. Indicator function not continuous and so not amenable to standard optimization tools. Challenge: Findlowcomplexitymethodswithacceptable performance 9 / 42

10 Outline 1 Talk Objective 2 Sparse Signal Recovery Problem 3 Applications 4 Computational Algorithms 5 Performance Evaluation 6 Conclusion 10 / 42

11 Applications Signal Representation (Mallat, Coifman, Wickerhauser, Donoho,...) EEG/MEG (Leahy, Gorodnitsky, Ioannides,...) Functional Approximation and Neural Networks (Chen, Natarajan, Cun, Hassibi,...) Bandlimited extrapolations and spectral estimation (Papoulis, Lee, Cabrera, Parks,...) Speech Coding (Ozawa, Ono, Kroon, Atal,...) Sparse channel equalization (Fevrier, Greenstein, Proakis, ) Compressive Sampling (Donoho, Candes, Tao..) 11 / 42

12 DFT Example N chosen to be 64 in this example. Measurement t: Dictionary Elements: t[n] = cos ω 0 n, n =0, 1, 2,.., N 1 ω 0 = 2π φ k = [1, e jω k, e j2ω k,.., e j(n 1)ω k ] T,ω k = 2π M Consider M =64, 128, 256 and 512 NOTE: The frequency components are included in the dictionary Φ for M =128, 256, and / 42

13 FFT Results FFT Results with Different M M=64 M=128 M=256 M= / 42

14 Magnetoencephalography (MEG) Given measured magnetic MEG fields outside Example of the head, the goal is to locate thegiven responsible measured current magnetic sources fields outside insideof the head, head. we would like to locate the responsible current sources inside the head. = MEG measurement point = Candidate source location 14 / 42

15 MEG Example At any given time, typicallymeg only aexample few sources are active (SPARSE). At any given time, typically only a few sources are active. = MEG measurement point = Candidate source location = Active source location 15 / 42

16 MEG Formulation Forming the overcomplete dictionary Φ The number of rows equals the number of sensors. The number of columns equals the number of possible source locations. Φ ij = the magnetic field measured at sensor i produced by a unit current at location j. We can compute Φ using a boundary element brain model and Maxwells equations. Many different combinations of current sources can produce the same observed magnetic field t. By finding the sparsest signal representation/basis, we find the smallest number of sources capable of producing the observed field. Such a representation is of neurophysiological significance 16 / 42

17 Compressive Sampling 17 / 42

18 Compressive Sampling Transform Coding Compressed Sensing w b A w A b t 17 / 42

19 Compressive Sampling Transform Coding Compressed Sensing w b Compressed Sensing w b A w A b t Compressive Sensing A w A b t 17 / 42

20 Compressive Sampling Transform Coding Compressed Sensing w b Compressed Sensing w b A w A b t Compressive Sensing A w A b t Computation : t w b 17 / 42

21 Outline 1 Talk Objective 2 Sparse Signal Recovery Problem 3 Applications 4 Computational Algorithms 5 Performance Evaluation 6 Conclusion 18 / 42

22 Potential Approaches Problem NP hard and so need alternate strategies Greedy Search Techniques: Matching Pursuit,Orthogonal Matching Pursuit Minimizing Diversity Measures: Indicator function not continuous. Define Surrogate Cost functions that are more tractable and whose minimization leads to sparse solutions, e.g. 1 minimization Bayesian Methods: Make appropriate Statistical assumptions on the solution and apply estimation techniques to identify the desired sparse solution 19 / 42

23 Greedy Search Methods: Matching Pursuit Select a column that is most aligned with the current residual t w Remove its contribution Stop when residual becomes small enough or if we have exceeded some sparsity threshold. Some Variations Matching Pursuit [Mallat & Zhang] Orthogonal Matching Pursuit [Pati et al.] Order Recursive Matching Pursuit (ORMP) 20 / 42

24 Inverse techniques For the systems of equations Φw = t, the solution set is characterized by {w s : w s =Φ + t + v, v N(Φ)}, where N (Φ) denotes the null space of Φ and Φ + =Φ T (ΦΦ T ) 1. Minimum Norm solution :Theminimum 2 norm solution w mn =Φ + t is a popular solution Noisy Case: regularized 2 norm solution often employed and is given by w reg =Φ T (ΦΦ T + λi ) 1 t Problem: Solution is not Sparse 21 / 42

25 Diversity Measures Functionals whose minimization leads to sparse solutions Many examples are found in the fields of economics, social science and information theory These functionals are concave which leads to difficult optimization problems 22 / 42

26 Examples of Diversity Measures (p 1) Diversity Measure E (p) (w) = M w l p, p 1 l=1 Gaussian Entropy Shannon Entropy H G (w) = M ln w l 2 l=1 H S (w) = M l=1 w l ln w l. where w l = w l 2 w 2 23 / 42

27 Diversity Minimization Noiseless Case min w E (p) (w) = M w l p subject to Φw = t l=1 Noisy Case min w t Φw 2 + λ M w l p l=1 p =1isaverypopularchoicebecauseoftheconvexnatureofthe optimization problem (Basis Pursuit and Lasso). 24 / 42

28 Bayesian Methods Maximum Aposteriori Approach (MAP) Assume a sparsity inducing prior on the latent variable w Develop an appropriate MAP estimation algorithm Empirical Bayes Assume a parameterized prior on the latent variable w (hyperparameters) Marginalize over the latent variable w and estimate the hyperparameters Determine the posterior distribution of w and obtain a point as the mean, mode or median of this density 25 / 42

29 Generalized Generalized Gaussian Gaussian Distribution Distribution Density function: Subgaussian: p > 2 and Supergaussian p : p < 2 ( f (x) ) p p x = 2σΓ( 1) exp exp x px 2 1 p σ p -p < 2: Super Gaussian -p > 2: Sub Gaussian p=0.5 p=1 p=2 p= / 42

30 Student t Distribution Density function: x px ( ) ν+1 Γ( 2 f (x) = ) 1 πνγ( ν 1+ x 2 ( ν+1 ) ν 2 2 ) = 1 = 2 = 5 Gaussian / 42

31 MAP using a Supergaussian prior Assuming a Gaussian likelihood model for f (t w), we can find MAP weight estimates w MAP = argmaxlog f (w t) w = argmax(log f (t w)+logf (w)) w = arg min w Φw t 2 + λ M w l p This is essentially a regularized LS framework. Interesting range for p is p 1. l=1 28 / 42

32 MAP Estimate: FOCal Underdetermined System Solver (FOCUSS) Approach involves solving a sequence of Regularized Weighted Minimum Norm problems q k+1 =argmin Φk+1 q t 2 + λq 2 q where Φ k+1 =ΦM k+1, and M k+1 =diag( w k.l 1 p 2. w k+1 = M k+1 q k+1. p =0isthe 0 minimization and p =1is 1 minimization 29 / 42

33 FOCUSS summary For p < 1, the solution is initial condition dependent Prior knowledge can be incorporated Minimum norm is a suitable choice Can retry with several initial conditions Computationally more complex than Matching Pursuit algorithms Sparsity versus tolerance tradeoff more involved Factor p allows a trade-off between the speed of convergence and the sparsity obtained 30 / 42

34 Convergence Errors vs. Structural Errors Convergence Errors vs. Structural Errors Convergence Error Structural Error -log p(w t) -log p(w t) w * w 0 w * w 0 w * = solution we have converged to w 0 = maximally sparse solution 31 / 42

35 Shortcomings of these Methods p =1 p < 1 Basis Pursuit/Lasso often suffer from structural errors. Therefore, regardless of initialization, we may never find the best solution. The FOCUSS class of algorithms suffers from numerous suboptimal local minima and therefore convergence errors. In the low noise limit, the number of local minima K satisfies M 1 M K +1, N N At most local minima, the number of nonzero coefficients is equal to N, thenumberofrowsinthedictionary. 32 / 42

36 Empirical Bayesian Method Main Steps Parameterized prior f (w γ) Marginalize f (t γ) = f (t, w γ)dw = f (t w)f (w γ)dw Estimate the hyperparameter ˆγ Determine the posterior density of the latent variable f (w t, ˆγ) Obtain point estimate of w Example: Sparse Bayesian Learning (SBL by Tipping) 33 / 42

37 Outline 1 Talk Objective 2 Sparse Signal Recovery Problem 3 Applications 4 Computational Algorithms 5 Performance Evaluation 6 Conclusion 34 / 42

38 DFT ExampleDFT Results Using FOCUSS 35 / 42

39 Empirical Tests Random overcomplete bases Φ and sparse weight vectors w 0 were generated and used to create target signals t, i.e., t =Φw 0 + SBL (Empirical Bayes) was compared with Basis Pursuit and FOCUSS (with various p values) in the task of recovering w / 42

40 Experiment Experiment 1: Comparison I: Noiseless withcomparison Noiseless Data Randomized Φ (20 rows by 40 40columns). Diversity of the true w 0 is 7. Results are from 1000 independent trials. NOTE: An error occurs whenever an algorithm converges to a solution NOTE: w An not error equaloccurs to w 0. whenever an algorithm converges to a solution w not equal to w / 42

41 Experiment II: Comparison with Noisy Data Experiment II: Noisy Comparisons Randomized Φ (20 (20 rows rows by by columns). Diversity 20 db of AWGN. the true w 0 is db Diversity AWGNof the true w is 7. Results Results are are from from independent trials. NOTE: We no longer distinguish convergence errors from structural NOTE: We no longer distinguish convergence errors from errors. structural errors. 38 / 42

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43 MEG Example Data based on CTF MEG system at UCSF with 275 scalp sensors. Forward model based on 40, 000 points (vertices of a triangular grid) and 3 different scale factors. Dimensions of lead field matrix (dictionary): 275 by 120, 000. Overcompleteness ratio approximately 436. Up to 40 unknown dipole components were randomly placed throughout the sample space. SBL was able to resolve roughly twice as many dipoles as the next best method (Ramirez 2005). 40 / 42

44 Outline 1 Talk Objective 2 Sparse Signal Recovery Problem 3 Applications 4 Computational Algorithms 5 Performance Evaluation 6 Conclusion 41 / 42

45 Summary Discussed role of sparseness in linear inverse problems Discussed Applications of Sparsity Discussed methods for computing sparse solutions Matching Pursuit Algorithms MAP methods (FOCUSS Algorithm and 1 minimization) Empirical Bayes (Sparse Bayesian Learning (SBL)) Expectation is that there will be continued growth in the application domain as well as in the algorithm development. 42 / 42