Compressed sensing and Bayesian experimental design

Proceedings of the 25th International Conference on Machine Learning

Volumn , Issue , 2008, Pages 912-919

  Seeger, Matthias W ^a   Nickisch, Hannes ^a  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; COMPRESSED SENSING; MACHINE LEARNING; STATISTICS; LEARNING SYSTEMS; ROBOT LEARNING; SIGNAL RECONSTRUCTION;

APPROXIMATE BAYESIAN; APPROXIMATE METHODS; BAYESIAN EXPERIMENTAL DESIGNS; COMPARATIVE STUDIES; COMPRESSIVE SENSING; EXPECTATION PROPAGATION; SEQUENTIAL PROJECTION; WAVELET COEFFICIENTS;

HEURISTIC METHODS;

APPROXIMATE METHODS; BAYESIAN; BAYESIAN METHODS; COMPARATIVE STUDIES; COMPRESSED SENSING; EXPECTATION PROPAGATION; EXPERIMENTAL DESIGNS; LARGE SIGNALS; NATURAL IMAGES; NOISE MODELS; OPTIMISATION; RANDOM MEASUREMENTS; SEQUENTIAL PROJECTIONS; SIGNAL COMPLEXITY; SPARSE SIGNALS; TOP DOWNS; WAVELET COEFFICIENTS;

EID: 56449114803     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390156.1390271     Document Type: Conference Paper

References (13)

1. Candès, E., & Romberg, J. (2004). Practical signal recovery from random projections. Proceedings of SPIE.
2. Candès, E., Romberg, J., & Tao, T. (2006). Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theo., 52, 489-509.
3. Donoho, D. (2006). Compressed sensing. IEEE Trans. Inf. Theo., 52, 1289-1306.
4. Duarte, M., Davenport, M., Takhar, D., Laska, J., Sun, T., Kelly, K., & Baraniuk, R. (2008). Single pixel imaging via compressive sampling. To appear in IEEE Signal Processing Magazine.
5. Elad, M. (2007). Optimized projections for compressed sensing. IEEE Transactions on Signal Processing.
6. Gerwinn, S., Macke, J., Seeger, M., & Bethge, M. (2008). Bayesian inference for spiking neuron models with a sparsity prior. Advances in NIPS 20.
7. Ji, S., & Carin, L. (2007). Bayesian compressive sensing and projection optimization. Proceedings of ICML 24.
8. Minka, T. (2001). Expectation propagation for approximate Bayesian inference. Uncertainty in AI 17.
9. Seeger, M. (2008). Bayesian inference and optimal design in the sparse linear model. To appear in Journal of Machine Learning Research.
10. Seeger, M., Steinke, F., & Tsuda, K. (2007). Bayesian inference and optimal design in the sparse linear model. AI and Statistics 11.
11. Simoncelli, E. (1999). Modeling the joint statistics of images in the Wavelet domain. Proceedings 44th SPIE (pp. 188-195).
12. Tipping, M. (2001). Sparse Bayesian learning and the relevance vector machine. J. M. Learn. Res., 1, 211-244.
13. Weiss, Y., Chang, H., & Freeman, W. (2007). Learning compressed sensing. Snowbird Learning Workshop, Allerton, CA.