Sparse geodesic paths

AAAI Fall Symposium - Technical Report

Volumn FS-09-04, Issue , 2009, Pages 2-9

  Davenport, Mark A ^a   Baraniuk, Richard G ^a  

^a Rice University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DISTANCE METRICS; GEODESIC PATHS; NATURAL IMAGES; SEMI-SUPERVISED; SHORTEST PATH; SIMILARITY MEASURE; SPARSE REPRESENTATION; TWO-POINT;

GRAPH THEORY;

GEODESY;

EID: 77954233581     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Aharon, M.; Elad, M.; and Bruckstein, A. 2006. K-svd: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Processing 54(11):4311-4322. (Pubitemid 44637761)
2. Baraniuk, R.; DeVore, R.; Kyriazis, G.; and Yu, X. 2002. Near best tree approximation. Advances in Comp. Math. 16(4):357-373.
3. Baraniuk, R.; Cevher, V; Duarte, M.; and Hegde, C. 2008. Model-based compressive sensing. Preprint.
4. Baraniuk, R. 1999. Optimal tree approximation with wavelets. In Proc. SPIE Wavelet Applications in Signal and Image Processing VII, volume 3813, 196-207.
5. Belkin, M., and Niyogi, P. 2003. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6).
6. Belkin, M., and Niyogi, P. 2004. Semi-supervised learning on Riemannian manifolds. Machine Learning Journal 56:209-239.
7. Blumensath, T., and Davies, M. E. 2007. Sampling theorems for signals from the union of linear subspaces. Preprint.
8. Candès, E. 2006. Compressive sampling. In Proc. Int. Congress of Mathematics, volume 3, 1433-1452.
9. Cohen, A.; Dahmen, W.; Daubechies, I.; and DeVore, R. 2001. Tree approximation and optimal encoding. Appl. Comp. Harm. Anal. 11(2):192-226.
10. Coifman, R. R., and Maggioni, M. 2006. Diffusion wavelets. Appl. Comput. Harmon. Anal. 21(1):53-94.
11. DeVore, R. 1998. Nonlinear approximation. Acta Numerica 7:51-150.
12. Donoho, D., and Grimes, C. 2003. Hessian Eigenmaps: Locally linear embedding techniques for high-dimensional data. Proc. Natl. Acad. Sci. USA 100(10):55915596.
13. Donoho, D., and Grimes, C. 2005. Image manifolds isometric to Euclidean space. J. Math. Imaging and Computer Vision 23(1):5-24. (Pubitemid 40539371)
14. Donoho, D. 1995. Denoising by soft-thresholding. IEEE. Trans. Inform. Theory 41(3):613-627.
15. Donoho, D. 2006. Compressed sensing. IEEE Trans. Inform. Theory 6(4): 1289-1306.
16. Eldar, Y. C., and Mishali, M. 2009. Robust recovery of signals from a structured union of subspaces. To appear in IEEE Trans. Inform. Theory.
17. J. Tenenbaum, V. d. S., and Langford, J. 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):23192323.
18. Mallat, S. 1999. A wavelet tour of signal processing. Academic Press.
19. Niyogi, P. 2008. Manifold regularization and semi-supervised learning: Some theoretical analyses. Technical Report TR-2008-01, Computer Science Dept., University of Chicago.
20. Olshausen, B., and Field, D. 1996. Emergence of simple-cell receptive field properties by learning a sparse representation. Nature 381:607-609. (Pubitemid 26177476)
21. Roweis, S., and Saul, L. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):23192323.
22. Weinberger, K., and Saul, L. 2006. Unsupervised learning of image manifolds by semidefinite programming. Int. J. Computer Vision Special Issue: Computer Vision and Pattern Recognition 70(1):7790. (Pubitemid 43855868)