Matching pursuit (MP) is a sparse approximation algorithm which finds the "best matching" projections of multidimensional data onto the span of an over-complete (i.e., redundant) dictionary . The basic idea is to approximately represent a signal from Hilbert space as a weighted sum of finitely many functions (called atoms) taken from . An approximation with atoms has the form
where is the th column of the matrix and is the scalar weighting factor (amplitude) for the atom . Normally, not every atom in will be used in this sum. Instead, matching pursuit chooses the atoms one at a time in order to maximally (greedily) reduce the approximation error. This is achieved by finding the atom that has the highest inner product with the signal (assuming the atoms are normalized), subtracting from the signal an approximation that uses only that one atom, and repeating the process until the signal is satisfactorily decomposed, i.e., the norm of the residual is small, where the residual after calculating and is denoted by
If converges quickly to zero, then only a few atoms are needed to get a good approximation to . Such sparse representations are desirable for signal coding and compression. More precisely, the sparsity problem that matching pursuit is intended to approximately solve is
where is the pseudo-norm (i.e. the number of nonzero elements of ). In the previous notation, the nonzero entries of are . Solving the sparsity problem exactly is NP-hard, which is why approximation methods like MP are used.
For comparison, consider the Fourier transform representation of a signal - this can be described using the terms given above, where the dictionary is built from sinusoidal basis functions (the smallest possible complete dictionary). The main disadvantage of Fourier analysis in signal processing is that it extracts only the global features of the signals and does not adapt to the analysed signals . By taking an extremely redundant dictionary, we can look in it for atoms (functions) that best match a signal .
If contains a large number of vectors, searching for the most sparse representation of is computationally unacceptable for practical applications. In 1993, Mallat and Zhang proposed a greedy solution that they named "Matching Pursuit." For any signal and any dictionary , the algorithm iteratively generates a sorted list of atom indices and weighting scalars, which form the sub-optimal solution to the problem of sparse signal representation.
In signal processing, the concept of matching pursuit is related to statistical projection pursuit, in which "interesting" projections are found; ones that deviate more from a normal distribution are considered to be more interesting.
- The algorithm converges (i.e. ) for any that is in the space spanned by the dictionary.
- The error decreases monotonically.
- As at each step, the residual is orthogonal to the selected filter, the energy conservation equation is satisfied for each :
- In the case that the vectors in are orthonormal, rather than being redundant, then MP is a form of principal component analysis
Matching pursuit has been applied to signal, image and video coding, shape representation and recognition, 3D objects coding, and in interdisciplinary applications like structural health monitoring. It has been shown that it performs better than DCT based coding for low bit rates in both efficiency of coding and quality of image. The main problem with matching pursuit is the computational complexity of the encoder. In the basic version of an algorithm, the large dictionary needs to be searched at each iteration. Improvements include the use of approximate dictionary representations and suboptimal ways of choosing the best match at each iteration (atom extraction). The matching pursuit algorithm is used in MP/SOFT, a method of simulating quantum dynamics.
A popular extension of Matching Pursuit (MP) is its orthogonal version: Orthogonal Matching Pursuit (OMP). The main difference from MP is that after every step, all the coefficients extracted so far are updated, by computing the orthogonal projection of the signal onto the subspace spanned by the set of atoms selected so far. This can lead to results better than standard MP, but requires more computation.
Extensions such as Multichannel MP and Multichannel OMP allow one to process multicomponent signals. An obvious extension of Matching Pursuit is over multiple positions and scales, by augmenting the dictionary to be that of a wavelet basis. This can be done efficiently using the convolution operator without changing the core algorithm.
Matching pursuit is related to the field of compressed sensing and has been extended by researchers in that community. Notable extensions are Orthogonal Matching Pursuit (OMP), Stagewise OMP (StOMP), compressive sampling matching pursuit (CoSaMP), Generalized OMP (gOMP), and Multipath Matching Pursuit (MMP).
- CLEAN algorithm
- Principal component analysis (PCA)
- Projection pursuit
- Image processing
- Signal processing
- Sparse approximation
- Mallat, S. G.; Zhang, Z. (1993). "Matching Pursuits with Time-Frequency Dictionaries". IEEE Transactions on Signal Processing. 1993 (12): 3397–3415. Bibcode:1993ITSP...41.3397M. doi:10.1109/78.258082.
- Perrinet, L. (2015). "Sparse models for Computer Vision". Biologically Inspired Computer Vision. 14: 319–346. arXiv:1701.06859. doi:10.1002/9783527680863.ch14. ISBN 9783527680863.
- Bergeaud, F.; Mallat, S. (1995). "Matching pursuit of images". Proc. International Conference on Image Processing. 1: 53–56. doi:10.1109/ICIP.1995.529037. ISBN 978-0-7803-3122-8.
- Neff, R.; Zakhor, A. (1997). "Very low bit-rate video coding based on matching pursuits". IEEE Transactions on Circuits and Systems for Video Technology. 7 (1): 158–171. doi:10.1109/76.554427.
- Mendels, F.; Vandergheynst, P.; Thiran, J.P. (2006). "Matching pursuit-based shape representation and recognition using scale-space". International Journal of Imaging Systems and Technology. 16 (5): 162–180. doi:10.1002/ima.20078.
- Tosic, I.; Frossard, P.; Vandergheynst, P. (2005). "Progressive coding of 3D objects based on over-complete decompositions". IEEE Transactions on Circuits and Systems for Video Technology. 16 (11): 1338–1349. doi:10.1109/tcsvt.2006.883502.
- Chakraborty, Debejyo; Kovvali, Narayan; Wei, Jun; Papandreou-Suppappola, Antonia; Cochran, Douglas; Chattopadhyay, Aditi (2009). "Damage Classification Structural Health Monitoring in Bolted Structures Using Time-frequency Techniques". Journal of Intelligent Material Systems and Structures. 20 (11): 1289–1305. doi:10.1177/1045389X08100044.
- Perrinet, L. U.; Samuelides, M.; Thorpe, S. (2002). "Sparse spike coding in an asynchronous feed-forward multi-layer neural network using Matching Pursuit". Neurocomputing. 57C: 125–34. doi:10.1016/j.neucom.2004.01.010.[permanent dead link]
- Lin, Jian-Liang; Hwang, Wen-Liang; Pei, Soo-Chang (2007). "Fast matching pursuit video coding by combining dictionary approximation and atom extraction". IEEE Transactions on Circuits and Systems for Video Technology. 17 (12): 1679–1689. CiteSeerX 10.1.1.671.9670. doi:10.1109/tcsvt.2007.903120.
- Wu, Yinghua; Batista, Victor S. (2003). "Matching-pursuit for simulations of quantum processes". J. Chem. Phys. 118 (15): 6720–6724. Bibcode:2003JChPh.118.6720W. doi:10.1063/1.1560636.
- Perrinet, L. P. (2010). "Role of homeostasis in learning sparse representations". Neural Computation. 22 (7): 1812–1836. arXiv:0706.3177. doi:10.1162/neco.2010.05-08-795. PMC 2929690. PMID 20235818.
- Aharon, M.; Elad, M.; Bruckstein, A.M. (2006). "The K-SVD: An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation". IEEE Transactions on Signal Processing. 54 (11): 4311–4322. Bibcode:2006ITSP...54.4311A. doi:10.1109/tsp.2006.881199.
- Pati, Y.; Rezaiifar, R.; Krishnaprasad, P. (1993). "Orthogonal Matching Pursuit: recursive function approximation with application to wavelet decomposition". Asilomar Conf. On Signals, Systems and Comput: 40–44. CiteSeerX 10.1.1.348.5735. doi:10.1109/acssc.1993.342465. ISBN 978-0-8186-4120-6.
- Davis, G.; Mallat, S.; Zhang, Z. (1994). "Adaptive time-frequency decompositions with matching pursuits". Optical Engineering. 33 (7): 2183. Bibcode:1994OptEn..33.2183D. doi:10.1117/12.173207.
- "Piecewise linear source separation", R. Gribonval, Proc. SPIE '03, 2003
- Tropp, Joel; Gilbert, A.; Strauss, M. (2006). "Algorithms for simultaneous sparse approximations ; Part I : Greedy pursuit". Signal Proc. – Sparse Approximations in Signal and Image Processing. 86 (3): 572–588. doi:10.1016/j.sigpro.2005.05.030.
- Perrinet, Laurent U. (2015). "Sparse models for Computer Vision". Biologically Inspired Computer Vision. pp. 319–346. arXiv:1701.06859. doi:10.1002/9783527680863.ch14. ISBN 9783527680863.
- Tropp, Joel A.; Gilbert, Anna C. (2007). "Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit" (PDF). IEEE Transactions on Information Theory. 53 (12): 4655–4666. doi:10.1109/tit.2007.909108.
- Donoho, David L.; Tsaig, Yaakov; Drori, Iddo; Jean-luc, Starck (2006). "Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit". IEEE Transactions on Information Theory. 58 (2): 1094–1121. doi:10.1109/tit.2011.2173241.
- Needell, D.; Tropp, J.A. (2009). "CoSaMP: Iterative signal recovery from incomplete and inaccurate samples". Applied and Computational Harmonic Analysis. 26 (3): 301–321. arXiv:0803.2392. doi:10.1016/j.acha.2008.07.002.
- Wang, J.; Kwon, S.; Shim, B. (2012). "Generalized Orthogonal Matching Pursuit". IEEE Transactions on Signal Processing. 60 (12): 6202–6216. arXiv:1111.6664. Bibcode:2012ITSP...60.6202J. doi:10.1109/TSP.2012.2218810.
- Kwon, S.; Wang, J.; Shim, B. (2014). "Multipath Matching Pursuit". IEEE Transactions on Information Theory. 60 (5): 2986–3001. arXiv:1308.4791. doi:10.1109/TIT.2014.2310482.