Kernel compressive sensing

2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Volumn , Issue , 2013, Pages 494-498

  Anaraki, Farhad Pourkamali ^a   Hughes, Shannon M ^a  

^a University of Colorado   (United States)

Author keywords

Compressed sensing; Dictionary learning; Kernel methods

Indexed keywords

DATA ACQUISITION; FUNCTIONS; IMAGE PROCESSING; INVERSE PROBLEMS; LEARNING SYSTEMS; RECOVERY; SIGNAL RECONSTRUCTION;

COMPRESSIVE SENSING; DICTIONARY LEARNING; FEATURE SPACE; KERNEL METHODS; LINEAR INVERSE PROBLEMS; NONLINEAR FUNCTIONS; NONLINEAR MANIFOLDS; RECOVERY METHODS;

COMPRESSED SENSING;

EID: 84897677677     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIP.2013.6738102     Document Type: Conference Paper

References (21)

1. D.L. Donoho, "Compressed sensing," IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1289-1306, April 2006
2. E.J. Candes and M.B.Wakin, "An introduction to compressive sampling," IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 21-30, March 2008
3. R.G. Baraniuk, V. Cevher, and M.B.Wakin, "Low-dimensional models for dimensionality reduction and signal recovery: A geometric perspective," Proceedings of the IEEE, vol. 98, no. 6, pp. 959-971, June 2010
4. J. B. Tenenbaum, V. de Silva, and J. C. Langford, "A global geometric framework for nonlinear dimensionality reduction," Science, 290 (5500), 2319-2323., Dec. 2000
5. G. Peyŕe, "Manifold models for signals and images," Comput. Vis. Image Underst., vol. 113, no. 2, pp. 249-260, Feb. 2009
6. M. Chen, J. Silva, J. Paisley, C. Wang, D. Dunson, and L. Carin, "Compressive sensing on manifolds using a nonparametric mixture of factor analyzers: Algorithm and performance bounds," IEEE Transactions on Signal Processing, vol. 58, no. 12, pp. 6140-6155, Dec. 2010
7. M. B. Wakin, "Manifold-based signal recovery and parameter estimation from compressive measurements," arXiv preprint arXiv:1002.1247, 2010
8. M. B. Wakin, "The geometry of low-dimensional signal models," Ph.D. Thesis, Rice University, 2006
9. B. Scḧolkopf, S. Mika, C.J.C. Burges, P. Knirsch, K.-R. Muller, G. Ratsch, and A.J. Smola, "Input space versus feature space in kernel-based methods," IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 1000-1017, Sep. 1999
10. B. Scḧolkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, Cambridge, MA, USA, 2001
11. B. Scḧolkopf, A. Smola, and K.M̈uller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Comput., vol. 10, no. 5, pp. 1299-1319, July 1998
12. H. Qi and S. Hughes, "Using the kernel trick in compressive sensing: Accurate signal recovery from fewer measurements," in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2011, pp. 3940-3943
13. M. Aharon, M. Elad, and A. Bruckstein, "K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation," IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4311-4322, Nov. 2006
14. H.V. Nguyen, V.M. Patel, N.M. Nasrabadi, and R. Chellappa, "Kernel dictionary learning," in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), March 2012, pp. 2021-2024
15. G. H. Bakir, J. Weston, and B. Scḧolkopf, "Learning to find pre-images," in Advances in Neural Information Processing Systems. 2004, pp. 449-456, MIT Press
16. Thomas Blumensath, "Accelerated iterative hard thresholding," Signal Processing, vol. 92, no. 3, pp. 752-756, 2012
17. Thomas Blumensath and Mike E. Davies, "Iterative hard thresholding for compressed sensing," Applied and Computational Harmonic Analysis, vol. 27, no. 3, pp. 265-274, 2009
18. P. Honeine and C. Richard, "Preimage problem in kernel-based machine learning," IEEE Signal Processing Magazine, vol. 28, no. 2, pp. 77-88, March 2011
19. J.A. Tropp, "Greed is good: Algorithmic results for sparse approximation," IEEE Transactions on Information Theory, vol. 50, no. 10, pp. 2231-2242, October 2004
20. J.A. Tropp and A.C. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Transactions on Information Theory, vol. 53, no. 12, pp. 4655-4666, Dec. 2007
21. Y. C. Eldar and G. Kutyniok, Eds., Compressed Sensing: Theory and Applications, Cambridge University Press, 2012.