Sparse representation for signal classification

Advances in Neural Information Processing Systems

Volumn , Issue , 2007, Pages 609-616

  Huang, Ke ^a   Aviyente, Selin ^a  

^a Michigan State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION TASKS; CORRUPTED SIGNALS; DE-NOISING; DISCRIMINATIVE METHODS; LINEAR DISCRIMINATIVE ANALYSIS; MISSING DATA; OBJECTIVE FUNCTIONS; OVER-COMPLETE; OVERCOMPLETE DICTIONARIES; RECONSTRUCTION PROPERTY; ROBUST CLASSIFICATION; SIGNAL CLASSIFICATION; SIGNAL CORRUPTION; SPARSE REPRESENTATION; THEORETICAL FRAMEWORK; THEORETICAL RESULT;

SIGNAL RECONSTRUCTION;

SIGNAL ANALYSIS;

EID: 84864043302     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. S. Mallat and Z. Zhang, "Matching pursuits with time-frequency dictionaries," IEEE Trans. on Signal Processing, vol. 41, pp. 3397-3415, 1993.
2. Y. Pati, R. Rezaiifar, and P. Krishnaprasad, "Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition," in 27th Annual Asilomar Conference on Signals, Systems, and Computers, 1993.
3. S. Chen, D. Donoho, and M. Saunders, "Atomic decomposition by basis pursuit," SIAM J. Scientific Computing, vol. 20, no. 1, pp. 33-61, 1999.
4. I. Drori and D. Donoho, "Solution of L1 minimization problems by LARS/Homotopy methods," in ICASSP, 2006, vol. 3, pp. 636-639.
5. M. Aharon, M. Elad, and A. Bruckstein, "The K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representation," IEEE Trans. On Signal Processing, to appear.
6. J. Starck, M. Elad, and D. Donoho, "Image decomposition via the combination of sparse representation and a variational approach," IEEE Trans. on Image Processing, vol. 14, no. 10, pp. 1570-1582, 2005.
7. Y. Li, A. Cichocki, and S. Amari, "Analysis of sparse representation and blind source separation," Neural Computation, vol. 16, no. 6, pp. 1193-1234, 2004.
8. B. Olshausen, P. Sallee, and M. Lewicki, "Learning sparse image codes using a wavelet pyramid architecture," in NIPS, 2001, pp. 887-893.
9. M. Elad and M. Aharon, "Image denoising via learned dictionaries and sparse representation," in CVPR, 2006.
10. M. Elad, B. Matalon, and M. Zibulevsky, "Image denoising with shrinkage and redundant representation," in CVPR, 2006.
11. D. Donoho and X. Huo, "Uncertainty principles and ideal atomic decomposition," IEEE Trans. on Information Theory, vol. 47, no. 7, pp. 2845-2862, 2001.
12. Y. Lin and D. Lee, "Bayesian L1-Norm sparse learning," in ICASSP, 2006, vol. 5, pp. 605-608.
13. D. Wipf and B. Rao, "Sparse bayesian learning for basis selection," IEEE Trans. on Signal Processing, vol. 52, no. 8, pp. 2153-2164, 2004.
14. R. Duda, P. Hart, and D. Stork, Pattern classification (2nd ed.), Wiley-Interscience, 2000.
15. P. Belhumeur, J. Hespanha, and D. Kriegman, "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, 1997.
16. A. Martinez and A. Kak, "PCA versus LDA," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228-233, 2001.
17. S. Fidler, D. Skocaj, and A. Leonardis, "Combining reconstructive and discriminative subspace methods for robust classification and regression by subsampling," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 28, no. 3, pp. 337-350, 2006.
18. M. Elad, J. Starck, P. Querre, and D.L. Donoho, "Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA)," Journal on Applied and Computational Harmonic Analysis, vol. 19, pp. 340-358, 2005.
19. A. Leonardis and H. Bischof, "Robust recognition using eigenimages," Computer Vision and Image Understanding, vol. 78, pp. 99-118, 2000.
20. J. Tropp, A. Gilbert, and M. Strauss, "Algorithms for simultaneous sparse approximation. part I: Greedy pursuit," Signal Processing, special issue on Sparse approximations in signal and image processing, vol. 86, no. 4, pp. 572-588, 2006.
21. J. Tropp, A. Gilbert, and M. Strauss, "Algorithms for simultaneous sparse approximation. part II: Convex relaxation," Signal Processing, special issue on Sparse approximations in signal and image processing, vol. 86, no. 4, pp. 589-602, 2006.
22. USPS Handwritten Digit Database, "available at: http://www.cs. toronto.edu/roweis/data.html,".
23. R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection," in IJCAI, 1995, pp. 1137-1145.
24. M. Tipping, "Sparse bayesian learning and the relevance vector machine," Journal of Machine Learning Research, vol. 1, pp. 211-244, 2001.