DOCSLIB.ORG

Accelerating Matching Pursuit for Multiple Time-Frequency Dictionaries

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Accelerating Matching Pursuit for Multiple Time-Frequency Dictionaries Proceedings of the 23rd International Conference on Digital Audio Effects (DAFx-20), Vienna, Austria, September 8–12, 2020 ACCELERATING MATCHING PURSUIT FOR MULTIPLE TIME-FREQUENCY DICTIONARIES ZdenˇekPr˚uša,Nicki Holighaus and Peter Balazs ∗ Acoustics Research Institute Austrian Academy of Sciences Vienna, Austria [email protected],[email protected],[email protected] ABSTRACT An overview of greedy algorithms, a class of algorithms MP Matching pursuit (MP) algorithms are widely used greedy meth- falls under, can be found in [10, 11] and in the context of audio ods to find K-sparse signal approximations in redundant dictionar- and music processing in [12, 13, 14]. Notable applications of MP ies. We present an acceleration technique and an implementation algorithms in the audio domain include analysis [15], [16], coding of the matching pursuit algorithm acting on a multi-Gabor dictio- [17, 18, 19], time scaling/pitch shifting [20] [21], source separation nary, i.e., a concatenation of several Gabor-type time-frequency [22], denoising [23], partial and harmonic detection and tracking dictionaries, consisting of translations and modulations of possi- [24]. bly different windows, time- and frequency-shift parameters. The We present a method for accelerating MP-based algorithms proposed acceleration is based on pre-computing and thresholding acting on a single Gabor-type time-frequency dictionary or on a inner products between atoms and on updating the residual directly concatenation of several Gabor dictionaries with possibly different in the coefficient domain, i.e., without the round-trip to thesig- windows and parameters. The main idea of the present accelera- nal domain. Previously, coefficient-domain residual updates have tion technique is performing the residual update in the coefficient been dismissed as having prohibitive memory requirements. By domain while exploiting the locality of the inner products between introducing an approximate update step, we can overcome this re- the atoms in the dictionaries and dismissing values below a user striction and greatly improve the performance of matching pursuit definable threshold. The idea of performing the residual updatein at a modest cost in terms of approximation quality per selected the coefficient domain using inner products between the atomsin atom. An implementation in C with Matlab and GNU Octave in- fact dates back to the original work by Mallat and Zhang [2, Ap- terfaces is available, outperforming the standard Matching Pursuit pendix E], but has always been dismissed as being prohibitively Toolkit (MPTK) by a factor of 3.5 to 70 in the tested conditions. storage expensive, as it generally requires storing pairwise inner Additionally, we provide experimental results illustrating the con- products between all dictionary elements. By exploiting the Ga- vergence of the implementation. bor structure of the dictionaries and dismissing insignificant inner products, it becomes feasible to store all significant inner products 1. INTRODUCTION in a lookup table and avoid atom synthesis and the residual re- analysis in every iteration of MP as it is usually done in practice. L Optimal K-sparse approximation of a signal x 2 R in an over- The size of the lookup table as well as the cost of computing it are complete dictionary of P normalized atoms (vectors) D = independent of the signal length and depend only on the parame- L×P [d0jd1j ::: jdP −1] 2 C , kdpk2 = 1 or, similarly, finding ters of the Gabor dictionaries. the minimal number of atoms that achieve a given error tolerance A freely available implementation in C (compatible with C99 kx − Dck2 ≤ E, are NP-hard problems [1]. Both problems can and C++11), can be found in the backend library of the Matlab/GNU be (sub-optimally) solved by employing greedy matching pursuit Octave Large Time-Frequency Analysis Toolbox (LTFAT, http: (MP) algorithms. The only difference is the choice of the stop- //ltfat.github.io) [25, 26] available individually at http: ping criterion. It has been shown that the basic version of MP [2] //ltfat.github.io/libltfat1. achieves an exponential approximation rate [1, 3, 4, 5]. In practice, without imposing any structure on the dictionary, the effectiveness We compare the performance of the proposed implementation of MP and its variants, see, e.g., [6, 7, 8, 9], quickly deteriorates with the Matching Pursuit Toolkit (MPTK) [27], the de-facto stan- when the problem dimensionality increases; either by increasing dard implementation of several MP based algorithms. By special- the input signal length L or the size of the dictionary P . Even with izing on multi-Gabor dictionaries, the presented algorithm is able structured dictionaries, and using fast algorithms in place of ma- to significantly outperform MPTK. trix operations, a naive implementation can still be prohibitively This manuscript is a shortened version of the paper [28], which inefficient; even decomposing a short audio clip can take hours. provides an extended discussion of the proposed algorithm includ- ing a worst case analysis of its convergence properties and more ∗ This work was supported by the Austrian Science Fund (FWF): Y 551–N13 and I 3067–N30. The authors thank Bob L. Sturm for shar- details about the provided implementation. ing his thoughts on the subject in a form of a blog Pursuits in the Null Space. Copyright: © 2020 ZdenˇekPr˚ušaet al. This is an open-access article distributed 1 Confer to http://ltfat.github.io/libltfat/group_ under the terms of the Creative Commons Attribution 3.0 Unported License, which _multidgtrealmp.html or http://ltfat.github.io/doc/ permits unrestricted use, distribution, and reproduction in any medium, provided the gabor/multidgtrealmp.html) for more information on the library original author and source are credited. and its Matlab interface. DAFx.1 Proceedings of the 23rd International Conference on Digital Audio Effects (DAFx-20), Vienna, Austria, September 8–12, 2020 2. PRELIMINARIES The procedure is repeated until the desired approximation er- ror is achieved or alternatively some other stopping criterion is Matrices and column vectors are denoted with capital and lower- met e.g. a sparsity or a selected inner product magnitude limits case bold upright letters, e.g., M, x, respectively. Conjugate trans- are reached. It is known that the matching pursuit (MP) algorithm pose is denoted with a star superscript, (x∗; M∗), scalar variables and its derivatives can benefit from pre-computing inner products with a capital or lowercase italics letter s; S. A single element of between the atoms in the dictionary G(k; j) = hdj ; dki i.e. from ∗ P ×P a matrix or a vector is selected using round brackets M(m; n), pre-computing the Gram matrix G = D D 2 C . With ∗ x(l). The index is always assumed to be applied modulo vec- %k = D rk denoting the coefficient-domain residual, the resid- tor length (or matrix size in the respective dimension) such that ual update step can be written as ([29, Ch. 12]) x(l) = x(l + kL) for l = 0;:::;L − 1 and k 2 Z. More- over, we will use two indices and subscript for vectors such that %k+1 = %k − c(pmax)G(•; pmax): (3) c(m; n) = c(m + nM) in order to transparently “matrixify” a M This modification has the advantage of removing the necessity of vector. Sub-vectors and sub-matrices are selected by an index set synthesizing the residual and recomputing the inner product in the denoted by a calligraphic letter e.g. x(P) and the m-th row or n- selection step. On the other hand, such approach is usually dis- th column of a matrix M are selected using the notation M(m; •) missed as impractical in the literature due to the high memory re- and M(•; n), respectively. Scalar-domain functions used on ma- quirements for storing the Gram matrix. We will show that this is trices or vectors are applied element-wise e.g. jxj2(l) = jx(l)j2. not the case for multi-Gabor dictionaries, see Section 3. Formally, The Euclidean inner product of two vectors in L is hx; yi = C the coefficient-domain matching pursuit algorithm is summarized ∗ PL−1 y x = l=0 x(l)y(l), where the overline denotes complex con- 2 in Alg. 1. The stopping criterion may contain several conditions, jugation, such that the 2-norm of a vector is defined by kxk2 = and the algorithm terminates if any of these conditions is met. hx; xi. Input: Input signal x, dictionary Gram matrix G = D∗D 2.1. Multi-Gabor Dictionaries Output: Solution vector c ∗ 2 L Initialization: c = 0, % = D x, E0 = kxk , k = 0 A Gabor dictionary D(g;a;M) generated from a window g 2 R , 0 2 while Stopping criterion not met do kgk2 = 1, time shift a and a number of modulations M is given as 1. Selection: pmax argmax j%k(p)j p i2πm(l−na)=M D(g;a;M)(l; m + nM) = g(l − na)e (1) 2. Update: for l = 0;:::;L − 1 and m = 0;:::;M − 1 for each n = (a) Solution: c(pmax) c(pmax) + %k(pmax) 0;:::;N − 1 N = L=a , where is the number of window time 2 shifts and the overall number of atoms is P = MN. Hence, the (b) Error: Ek+1 Ek − j%k(pmax)j redundancy of a dictionary is P=L = M=a. A multi-Gabor dic- (c) Residual: %k+1 %k − %k(pmax)G(•; pmax) tionary consists of W Gabor dictionaries, i.e., k k + 1 D(g1;a1;M1) D(g2;a2;M2) ::: D(gW ;aW ;MW ) (2) end Algorithm 1: Coefficient-Domain Matching Pursuit and we will also use a shortened notation Dw = D(gw;aw;Mw). Generally, aw;Mw need only be divisors of L.
Load more

Recommended publications
  • Paper, We Present Data Fusion Across Multiple Signal Sources
    68 IEEE JOURNAL OF SOLID-STATE CIRCUITS, VOL. 51, NO. 1, JANUARY 2016 A Configurable 12–237 kS/s 12.8 mW Sparse-Approximation Engine for Mobile Data Aggregation of Compressively Sampled Physiological Signals Fengbo Ren, Member, IEEE, and Dejan Markovic,´ Member, IEEE Abstract—Compressive sensing (CS) is a promising technology framework, the CS framework has several intrinsic advantages. for realizing low-power and cost-effective wireless sensor nodes First, random encoding is a universal compression method that (WSNs) in pervasive health systems for 24/7 health monitoring. can effectively apply to all compressible signals regardless of Due to the high computational complexity (CC) of the recon- struction algorithms, software solutions cannot fulfill the energy what their sparse domain is. This is a desirable merit for the efficiency needs for real-time processing. In this paper, we present data fusion across multiple signal sources. Second, sampling a 12—237 kS/s 12.8 mW sparse-approximation (SA) engine chip and compression can be performed at the same stage in CS, that enables the energy-efficient data aggregation of compressively allowing for a sampling rate that is significantly lower than the sampled physiological signals on mobile platforms. The SA engine Nyquist rate. Therefore, CS has a potential to greatly impact chip integrated in 40 nm CMOS can support the simultaneous reconstruction of over 200 channels of physiological signals while the data acquisition devices that are sensitive to cost, energy consuming <1% of a smartphone’s power budget. Such energy- consumption, and portability, such as wireless sensor nodes efficient reconstruction enables two-to-three times energy saving (WSNs) in mobile and wearable applications [5].
    [Show full text]
  • Improved Greedy Algorithms for Sparse Approximation of a Matrix in Terms of Another Matrix
    Improved Greedy Algorithms for Sparse Approximation of a Matrix in terms of Another Matrix Crystal Maung Haim Schweitzer Department of Computer Science Department of Computer Science The University of Texas at Dallas The University of Texas at Dallas Abstract We consider simultaneously approximating all the columns of a data matrix in terms of few selected columns of another matrix that is sometimes called “the dic- tionary”. The challenge is to determine a small subset of the dictionary columns that can be used to obtain an accurate prediction of the entire data matrix. Previ- ously proposed greedy algorithms for this task compare each data column with all dictionary columns, resulting in algorithms that may be too slow when both the data matrix and the dictionary matrix are large. A previously proposed approach for accelerating the run time requires large amounts of memory to keep temporary values during the run of the algorithm. We propose two new algorithms that can be used even when both the data matrix and the dictionary matrix are large. The first algorithm is exact, with output identical to some previously proposed greedy algorithms. It takes significantly less memory when compared to the current state- of-the-art, and runs much faster when the dictionary matrix is sparse. The second algorithm uses a low rank approximation to the data matrix to further improve the run time. The algorithms are based on new recursive formulas for computing the greedy selection criterion. The formulas enable decoupling most of the compu- tations related to the data matrix from the computations related to the dictionary matrix.
    [Show full text]
  • Privacy Preserving Identification Using Sparse Approximation With
    Privacy Preserving Identification Using Sparse Approximation with Ambiguization Behrooz Razeghi, Slava Voloshynovskiy, Dimche Kostadinov and Olga Taran Stochastic Information Processing Group, Department of Computer Science, University of Geneva, Switzerland behrooz.razeghi, svolos, dimche.kostadinov, olga.taran @unige.ch f g Abstract—In this paper, we consider a privacy preserving en- Owner Encoder Public Storage coding framework for identification applications covering biomet- +1 N M λx λx L M rics, physical object security and the Internet of Things (IoT). The X × − A × ∈ X 1 ∈ A proposed framework is based on a sparsifying transform, which − X = x (1) , ..., x (m) , ..., x (M) a (m) = T (Wx (m)) n A = a (1) , ..., a (m) , ..., a (M) consists of a trained linear map, an element-wise nonlinearity, { } λx { } and privacy amplification. The sparsifying transform and privacy L p(y (m) x (m)) Encoder | amplification are not symmetric for the data owner and data user. b Schematic Decoding List Probe +1 We demonstrate that the proposed approach is closely related (Private Decoding) y = x (m) + z d (a (m) , b) γL λy λy ≤ − to sparse ternary codes (STC), a recent information-theoretic 1 p (positions) − ´x y = ´x (Pubic Decoding) 1 m M (y) concept proposed for fast approximate nearest neighbor (ANN) ≤ ≤ L Data User b = Tλy (Wy) search in high dimensional feature spaces that being machine learning in nature also offers significant benefits in comparison Fig. 1: Block diagram of the proposed model. to sparse approximation and binary embedding approaches. We demonstrate that the privacy of the database outsourced to a for example biometrics, which being disclosed once, do not server as well as the privacy of the data user are preserved at a represent any more a value for the related security applications.
    [Show full text]
  • Column Subset Selection Via Sparse Approximation of SVD
    Column Subset Selection via Sparse Approximation of SVD A.C¸ivrila,∗, M.Magdon-Ismailb aMeliksah University, Computer Engineering Department, Talas, Kayseri 38280 Turkey bRensselaer Polytechnic Institute, Computer Science Department, 110 8th Street Troy, NY 12180-3590 USA Abstract Given a real matrix A 2 Rm×n of rank r, and an integer k < r, the sum of the outer products of top k singular vectors scaled by the corresponding singular values provide the best rank-k approximation Ak to A. When the columns of A have specific meaning, it might be desirable to find good approximations to Ak which use a small number of columns of A. This paper provides a simple greedy algorithm for this problem in Frobenius norm, with guarantees on( the performance) and the number of columns chosen. The algorithm ~ k log k 2 selects c columns from A with c = O ϵ2 η (A) such that k − k ≤ k − k A ΠC A F (1 + ϵ) A Ak F ; where C is the matrix composed of the c columns, ΠC is the matrix projecting the columns of A onto the space spanned by C and η(A) is a measure related to the coherence in the normalized columns of A. The algorithm is quite intuitive and is obtained by combining a greedy solution to the generalization of the well known sparse approximation problem and an existence result on the possibility of sparse approximation. We provide empirical results on various specially constructed matrices comparing our algorithm with the previous deterministic approaches based on QR factorizations and a recently proposed randomized algorithm.
    [Show full text]
  • Modified Sparse Approximate Inverses (MSPAI) for Parallel
    Technische Universit¨atM¨unchen Zentrum Mathematik Modified Sparse Approximate Inverses (MSPAI) for Parallel Preconditioning Alexander Kallischko Vollst¨andiger Abdruck der von der Fakult¨atf¨ur Mathematik der Technischen Universit¨at M¨unchen zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) genehmigten Dissertation. Vorsitzender: Univ.-Prof. Dr. Peter Rentrop Pr¨ufer der Dissertation: 1. Univ.-Prof. Dr. Thomas Huckle 2. Univ.-Prof. Dr. Bernd Simeon 3. Prof. Dr. Matthias Bollh¨ofer, Technische Universit¨atCarolo-Wilhelmina zu Braunschweig (schriftliche Beurteilung) Die Dissertation wurde am 15.11.2007 bei der Technischen Universit¨ateingereicht und durch die Fakult¨atf¨urMathematik am 18.2.2008 angenommen. ii iii Abstract The solution of large sparse and ill-conditioned systems of linear equations is a central task in numerical linear algebra. Such systems arise from many applications like the discretiza- tion of partial differential equations or image restoration. Herefore, Gaussian elimination or other classical direct solvers can not be used since the dimension of the underlying co- 3 efficient matrices is too large and Gaussian elimination is an O n algorithm. Iterative solvers techniques are an effective remedy for this problem. They allow to exploit sparsity, bandedness, or block structures, and they can be parallelized much easier. However, due to the matrix being ill-conditioned, convergence becomes very slow or even not be guaranteed at all. Therefore, we have to employ a preconditioner. The sparse approximate inverse (SPAI) preconditioner is based on Frobenius norm mini- mization. It is a well-established preconditioner, since it is robust, flexible, and inherently parallel. Moreover, SPAI captures meaningful sparsity patterns automatically.
    [Show full text]
  • A New Algorithm for Non-Negative Sparse Approximation Nicholas Schachter
    A New Algorithm for Non-Negative Sparse Approximation Nicholas Schachter To cite this version: Nicholas Schachter. A New Algorithm for Non-Negative Sparse Approximation. 2020. hal- 02888300v1 HAL Id: hal-02888300 https://hal.archives-ouvertes.fr/hal-02888300v1 Preprint submitted on 2 Jul 2020 (v1), last revised 9 Jun 2021 (v5) HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. A New Algorithm for Non-Negative Sparse Approximation Nicholas Schachter July 2, 2020 Abstract In this article we introduce a new algorithm for non-negative sparse approximation problems based on a combination of the approaches used in orthogonal matching pursuit and basis de-noising pursuit towards solving sparse approximation problems. By taking advantage of structural properties inherent to non-negative sparse approximation problems, a branch and bound (BnB) scheme is developed that enables fast and accurate recovery of underlying dictionary atoms, even in the presence of noise. Detailed analysis of the performance of the algorithm is discussed, with attention specically paid to situations in which the algorithm will perform better or worse based on the properties of the dictionary and the required sparsity of the solution.
    [Show full text]
  • Efficient Implementation of the K-SVD Algorithm Using
    E±cient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit Ron Rubinstein¤, Michael Zibulevsky¤ and Michael Elad¤ Abstract The K-SVD algorithm is a highly e®ective method of training overcomplete dic- tionaries for sparse signal representation. In this report we discuss an e±cient im- plementation of this algorithm, which both accelerates it and reduces its memory consumption. The two basic components of our implementation are the replacement of the exact SVD computation with a much quicker approximation, and the use of the Batch-OMP method for performing the sparse-coding operations. - Technical Report CS-2008-08.revised 2008 Batch-OMP, which we also present in this report, is an implementation of the Orthogonal Matching Pursuit (OMP) algorithm which is speci¯cally optimized for sparse-coding large sets of signals over the same dictionary. The Batch-OMP imple- mentation is useful for a variety of sparsity-based techniques which involve coding large numbers of signals. In the report, we discuss the Batch-OMP and K-SVD implementations and analyze their complexities. The report is accompanied by Matlabr toolboxes which implement these techniques, and can be downloaded at http://www.cs.technion.ac.il/~ronrubin/software.html. 1 Introduction Sparsity in overcomplete dictionaries is the basis for a wide variety of highly e®ective signal and image processing techniques. The basic model suggests that natural signals can be e±ciently explained as linear combinations of prespeci¯ed atom signals, where the linear coe±cients are sparse (most of them zero). Formally, if x is a column signal and D is the dictionary (whose columns are the atom signals), the sparsity assumption can be described by the following sparse approximation problem, 2 γ^ = Argmin γ 0 Subject To x Dγ ² : (1.1) γ k k k ¡ k2 · Technion - Computer Science Department In this formulation, γ is the sparse representation of x, ² the error tolerance, and k ¢ k0 is the `0 pseudo-norm which counts the non-zero entries.
    [Show full text]
  • Regularized Dictionary Learning for Sparse Approximation
    16th European Signal Processing Conference (EUSIPCO 2008), Lausanne, Switzerland, August 25-29, 2008, copyright by EURASIP REGULARIZED DICTIONARY LEARNING FOR SPARSE APPROXIMATION M. Yaghoobi, T. Blumensath, M. Davies Institute for Digital Communications, Joint Research Institute for Signal and Image Processing, University of Edinburgh, UK ABSTRACT keeping the dictionary fixed. This is followed by a second step in Sparse signal models approximate signals using a small number of which the sparse coefficients are kept fixed and the dictionary is elements from a large set of vectors, called a dictionary. The suc- optimized. This algorithm runs for a specific number of alternating cess of such methods relies on the dictionary fitting the signal struc- optimizations or until a specific approximation error is reached. The ture. Therefore, the dictionary has to be designed to fit the signal proposed method is based on such an alternating optimization (or class of interest. This paper uses a general formulation that allows block-relaxed optimization) method with some advantages over the the dictionary to be learned form the data with some a priori in- current methods in the condition and speed of convergence. formation about the dictionary. In this formulation a universal cost If the set of training samples is {y(i) : 1 ≤ i ≤ L}, where L function is proposed and practical algorithms are presented to min- is the number of training vectors, then sparse approximations are imize this cost under different constraints on the dictionary. The often found (for all i : 1 ≤ i ≤ L ) by, proposed methods are compared with previous approaches using (i) (i) 2 p synthetic and real data.
    [Show full text]
  • Structured Compressed Sensing - Using Patterns in Sparsity
    Structured Compressed Sensing - Using Patterns in Sparsity Johannes Maly Technische Universit¨atM¨unchen, Department of Mathematics, Chair of Applied Numerical Analysis [email protected] CoSIP Workshop, Berlin, Dezember 9, 2016 Overview Classical Compressed Sensing Structures in Sparsity I - Joint Sparsity Structures in Sparsity II - Union of Subspaces Conclusion Johannes Maly Structured Compressed Sensing - Using Patterns in Sparsity 2 of 44 Classical Compressed Sensing Overview Classical Compressed Sensing Structures in Sparsity I - Joint Sparsity Structures in Sparsity II - Union of Subspaces Conclusion Johannes Maly Structured Compressed Sensing - Using Patterns in Sparsity 3 of 44 Classical Compressed Sensing Compressed Sensing N Let x 2 R be some unknown k-sparse signal. Then, x can be recovered from few linear measurements y = A · x m×N m where A 2 R is a (random) matrix, y 2 R is the vector of measurements and m N. Johannes Maly Structured Compressed Sensing - Using Patterns in Sparsity 4 of 44 Classical Compressed Sensing Compressed Sensing N Let x 2 R be some unknown k-sparse signal. Then, x can be recovered from few linear measurements y = A · x m×N m where A 2 R is a (random) matrix, y 2 R is the vector of measurements and m N. It is sufficient to have N m Ck log & k measurements to recover x (with high probability) by greedy strategies, e.g. Orthogonal Matching Pursuit, or convex optimization, e.g. `1-minimization. Johannes Maly Structured Compressed Sensing - Using Patterns in Sparsity 5 of 44 OMP INPUT: matrix A; measurement vector y: INIT: T0 = ;; x0 = 0: ITERATION: until stopping criterion is met T jn+1 arg maxj2[N] (A (y − Axn))j ; Tn+1 Tn [ fjn+1g; xn+1 arg minz2RN fky − Azk2; supp(z) ⊂ Tn+1g : OUTPUT: then ~-sparse approximationx ^ := xn~ Classical Compressed Sensing Orthogonal Matching Pursuit OMP is a simple algorithm that tries to find the true support of x by k greedy steps.
    [Show full text]
  • Sparse Estimation for Image and Vision Processing
    Sparse Estimation for Image and Vision Processing Julien Mairal Inria, Grenoble SPARS summer school, Lisbon, December 2017 Julien Mairal Sparse Estimation for Image and Vision Processing 1/187 Course material (freely available on arXiv) J. Mairal, F. Bach and J. Ponce. Sparse Modeling for Image and Vision Processing. Foundations and Trends in Computer Graphics and Vision. 2014. F. Bach, R. Jenatton, J. Mairal, and G. Obozinski. Optimization with sparsity-inducing penalties. Foundations and Trends in Machine Learning, 4(1). 2012. Julien Mairal Sparse Estimation for Image and Vision Processing 2/187 Outline 1 A short introduction to parsimony 2 Discovering the structure of natural images 3 Sparse models for image processing 4 Optimization for sparse estimation 5 Application cases Julien Mairal Sparse Estimation for Image and Vision Processing 3/187 Part I: A Short Introduction to Parcimony Julien Mairal Sparse Estimation for Image and Vision Processing 4/187 1 A short introduction to parsimony Early thoughts Sparsity in the statistics literature from the 60’s and 70’s Wavelet thresholding in signal processing from 90’s The modern parsimony and the ℓ1-norm Structured sparsity Compressed sensing and sparse recovery 2 Discovering the structure of natural images 3 Sparse models for image processing 4 Optimization for sparse estimation 5 Application cases Julien Mairal Sparse Estimation for Image and Vision Processing 5/187 Early thoughts (a) Dorothy Wrinch (b) Harold Jeffreys 1894–1980 1891–1989 The existence of simple laws is, then, apparently, to be regarded as a quality of nature; and accordingly we may infer that it is justifiable to prefer a simple law to a more complex one that fits our observations slightly better.
    [Show full text]
  • Exact Sparse Approximation Problems Via Mixed-Integer Programming: Formulations and Computational Performance
    Exact Sparse Approximation Problems via Mixed-Integer Programming: Formulations and Computational Performance Sébastien Bourguignon, Jordan Ninin, Hervé Carfantan, Marcel Mongeau To cite this version: Sébastien Bourguignon, Jordan Ninin, Hervé Carfantan, Marcel Mongeau. Exact Sparse Approxi- mation Problems via Mixed-Integer Programming: Formulations and Computational Performance. IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2016, 64 (6), pp.1405-1419. 10.1109/TSP.2015.2496367. hal-01254856 HAL Id: hal-01254856 https://hal.archives-ouvertes.fr/hal-01254856 Submitted on 12 Jan 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. 1 Exact Sparse Approximation Problems via Mixed-Integer Programming: Formulations and Computational Performance Sebastien´ Bourguignon, Jordan Ninin, Herve´ Carfantan, and Marcel Mongeau, Member, IEEE Abstract—Sparse approximation addresses the problem of too difficult in practical large-scale instances. Indeed, the approximately fitting a linear model with a solution having as few Q brute-force approach that amounts to exploring all the K non-zero components as possible. While most sparse estimation possible combinations, is computationally prohibitive. In the algorithms rely on suboptimal formulations, this work studies the abundant literature on sparse approximation, much work has performance of exact optimization of `0-norm-based problems through Mixed-Integer Programs (MIPs).
    [Show full text]
  • Bridging the Gap Between Numerical Linear Algebra, Theoretical Computer Science, and Data Applications
    From SIAM News, Volume 39, Number 8, October 2006 Bridging the Gap Between Numerical Linear Algebra, Theoretical Computer Science, and Data Applications By Gene H. Golub, Michael W. Mahoney, Petros Drineas, and Lek-Heng Lim The Workshop on Algorithms for Modern Massive Data Sets (MMDS 2006), sponsored by the National Science Foundation, Yahoo! Research, and Ask.com, was held at Stanford University, June 21–24. The objectives were to explore novel techniques for modeling and ana- lyzing massive, high-dimensional, and nonlinearly structured data sets, and to bring together computer scientists, computational and applied mathematicians, statisticians, and practitioners to promote cross-fertilization of ideas. The program, with 45 talks and 24 poster presentations, drew 232 participants—far exceeding the anticipated 75! MMDS 2006 grew out of discussions among the four organizers (who are also the authors of this article) about the complementary perspec- tives brought by the numerical linear algebra (NLA) and the theoretical computer science (TCS) communities to linear algebra and matrix com- putations. These discussions were motivated by data applications, and, in particular, by technological developments over the last two decades (in both scientific and Internet domains) that permit the automatic generation of very large data sets. Such data are often modeled as matrices: An m × n real-valued matrix A provides a natural structure for encoding information about m objects, each of which is described by n features. In genetics, for example, microarray expression data can be represented in such a framework, with Aij representing the expression level of the ith gene in the jth experimental condition.
    [Show full text]