Dictionary optimization for block-sparse representations

AAAI Fall Symposium - Technical Report

Volumn FS-10-06, Issue , 2010, Pages 50-58

  Rosenblum, Kevin ^a   Zelnik Manor, Lihi ^a   Eldar, Yonina C ^a  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

FACE RECOGNITION; ITERATIVE METHODS; LEARNING SYSTEMS;

BLOCK SPARSE; BLOCK STRUCTURES; DICTIONARY LEARNING; LEARNING METHODS; MOTION SEGMENTATION; PRIOR KNOWLEDGE; SIGNALS OF INTERESTS;

LEARNING ALGORITHMS;

EID: 79960134130     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. Aharon, M.; Elad, M.; and Bruckstein, A. M. 2006a. On the uniqueness of overcomplete dictionaries and a practical way to retrieve them. Journal of Linear Algebra and Applications 416:4867.
2. Aharon, M.; Elad, M.; and Bruckstein, A. M. 2006b. The K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representations. IEEE Trans. SP 54(11).
3. Basri, R., and Jacobs, D. 2003. Lambertian refelectances and linear subspaces. IEEE Transactions On Pattern Analysis And Machine Intelligence 25(2):383-390.
4. Candes, E.; Romberg, J.; and Tao, T. 2006. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inform. Theory 52:489-509. (Pubitemid 43174093)
5. Chen, S. S.; Donoho, D. L.; and Saunders, M. A. 1999. Atomic decomposition by basis pursuit. SIAM J. Sci. Cornput. 20(1):33-61.
6. Donoho, D. 2006. Compressed sensing. IEEE Trans. Inform. Theory 52(4):1289-1306.
7. Duarte-Carvajalino, J. M., and Sapiro, G. 2008. Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization. IMA Preprint Series (2211).
8. Duda, H., and Hart, P. 2001. Stork, Pattern Classification.
9. Eldar, Y. C., and Bölcskei, H. 2009. Block-sparsity: Coherence and efficient recovery. IEEE International Conference on Acoustics, Speech, and Signal Processing 0:2885-2888.
10. Eldar, Y. C., and Mishali, M. 2009. Robust recovery of signals from a structured union of subspaces. IEEE Trans. Inform. Theory 55(11):5302-5316.
11. Eldar, Y. C.; Kuppinger, P.; and Bölcskei, H. 2010. Blocksparse signals: Uncertainty relations and efficient recovery. IEEE Trans. Sig. Proc.
12. Elhamifar, R. V. E. 2009. Sparse subspace clustering. IEEE Conference on Computer Vision and Pattern Recognition.
13. Engan, K.; Aase, S. O.; and Hakon-Husoy, J. H. 1999. Method of optimal directions for frame design. IEEE Int. Conf. Acoust., Speech, Signal Process 5:2443-2446.
14. Gedalyahu, K., and Eldar, Y. C. 2010. Time delay estimation from low rate samples: A union of subspaces approach. IEEE Trans. Signal Process. 58(6):3017-3031.
15. Johnson, S. C. 1967. Hierarchical clustering schemes. Psychometrika 32:241-254.
16. Landau, H. J. 1967. Necessary density conditions for sampling and interpolation of certain entire functions. Acta Math. 117(1):37-52.
17. Lesage, S.; Gribonval, R.; Bimbot, F; and Benaroya, L. 2005. Learning unions of orthonormal bases with thresholded singular value decomposition. IEEE Conf. on Acoustics, Speech and Signal Processing.
18. Ma, Y.; Derksen, H.; Hong, W.; and Wright, J. 2007. Segmentation of multivariate mixed data via lossy coding and compression. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(9):15461562.
19. Mallat, S. G., and Zhang, Z. 1993. Matching pursuits and time-frequency dictionaries. IEEE Trans. Sig. Proc. 41(12):33973415.
20. Mishali, M., and Eldar, Y. C. 2009. Blind multiband signal reconstruction: Compressed sensing for analog signals. IEEE Trans. Sig. Proc. 57(3):993-1009.
21. Mishali, M., and Eldar, Y. C. 2010. From theory to practice: Sub-Nyquist sampling of sparse wideband analog signals. IEEE Journal of Selected Topics in Signal Processing 4(2):375-391.
22. Olshausen, B., and Field, D. 1996. Natural image statistics and efficient coding. Network: Comput. Neural Syst. 2(7):333-339.
23. Parvaresh, F.; Vikalo, H.; Misra, S.; and Hassibi, B. 2008. Recovering sparse signals using sparse measurement matrices in compressed dna microarrays. IEEE Journal of Selected Topics in Signal Processing.
24. Rauhut, H., and Eldar, Y. 2010. Average case analysis of multichannel sparse recovery using convex relaxation. IEEE Trans. Inform. Theory 56(1):505-519.
25. Stojnic, M.; Parvaresh, F.; and Hassibi, B. 2009. On the reconstruction of block-sparse signals with an optimal number of measurements. IEEE Trans. Sig. Proc. 57(8):3075-3085.
26. Tropp, J. 2004. Greed is good: Algorithmic results for sparse approximation. IEEE Trans. Inform. Theory 50(10):2231-2242.
27. Vidal, R., and Ma, Y. 2006. A unified algebraic approach to 2-D and 3-D motion segmentation and estimation. Journal of Mathematical Imaging and Vision 25(3):403-421. (Pubitemid 44728465)
28. Vidal, R.; Ma, Y.; and Sastry, S. 2005. Generalized principal component analysis (GPCA). IEEE Transactions on Pattern Analysis and Machine Intelligence 27(11).
29. Yang, A. Y.; Wright, J.; Ma, Y.; and Sastry, S. 2007. Feature selection in face recognition: A sparse representation perspective. UC Berkeley Tech Report.
30. Yuan, M., and Lin, Y. 2006. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology 68(1):49-67. (Pubitemid 43415335)