Multi-task compressive sensing with Dirichlet process priors

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Multi-task compressive sensing with Dirichlet process priors

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ICML; Vol. 307 archive
Proceedings of the 25th international conference on Machine learning table of contents

Helsinki, Finland

Pages 768-775

Year of Publication: 2008

ISBN:978-1-60558-205-4

Authors

Yuting Qi	Duke University, Durham, NC
Dehong Liu	Duke University, Durham, NC
David Dunson	Duke University, Durham, NC
Lawrence Carin	Duke University, Durham, NC

Sponsors

: Yahoo!
: Xerox
IBM : IBM
: NSF
Microsoft Research : Microsoft Research
: Machine Learning Journal/Springer
: Pascal
: University of Helsinki
: Federation of Finnish Learned Societies
: Intel Corporation
: Google
: Helsinki Institute for Information Technology

Publisher

ACM New York, NY, USA

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ABSTRACT

Compressive sensing (CS) is an emerging £eld that, under appropriate conditions, can signi£cantly reduce the number of measurements required for a given signal. In many applications, one is interested in multiple signals that may be measured in multiple CS-type measurements, where here each signal corresponds to a sensing "task". In this paper we propose a novel multitask compressive sensing framework based on a Bayesian formalism, where a Dirichlet process (DP) prior is employed, yielding a principled means of simultaneously inferring the appropriate sharing mechanisms as well as CS inversion for each task. A variational Bayesian (VB) inference algorithm is employed to estimate the full posterior on the model parameters.

REFERENCES

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	1	Baron, D., Wakin, M. B., Duarte, M. F., Sarvotham, S., & Baraniuk, R. G. (2005). Distributed compressed sensing.
	2	Beal, M. J. (2003). Variational algorithms for approximate bayesian inference. Doctoral dissertation, Gatsby Computational Neuroscience Unit, Univ. College London.
	3	David M. Blei , Michael I. Jordan, Variational methods for the Dirichlet process, Proceedings of the twenty-first international conference on Machine learning, p.12, July 04-08, 2004, Banff, Alberta, Canada [doi>10.1145/1015330.1015439]
	4	Candes, E. (2006). Compressive sensing. Proc. of ICM, 3, 1433--1452.
	5	Candes, E., Romberg, J., & Tao, T. (2006). Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans. on Inform. Theory, 52, 489--502.
	6	Charilaos, C. (1999). Jpeg2000 tutorial. ICIP.
	7	Donoho, D. L. (2006). Compressed sensing. IEEE Trans. Information Theory, 52, 1289--1306.
	8	Donoho, D. L., Tsaig, Y., Drori, I., & Starck, J.-C. (2006). Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit.
	9	Escobar, M. D., & West, M. (1995). Bayesian density estimation and inference using mixtures. JASA, 90, 577--588.
	10	Gilks, W. R., Richardson, S., & Spiegelhalter, D. J. (1996). Introducing markov chain monte carlo. In Markov chain monte carlo in practice. London, U.K.: Chapman Hall.
	11	Ishwaran, H., & James, L. F. (2001). Gibbs sampling methods for stick-breaking priros. JASA, 96, 161--173.
	12	Ji, S., Dunson, D., & Carin, L. (2007a). Multi-task compressive sensing. Submit to IEEE Trans. on SP.
	13	Ji, S., Xue, Y., & Carin, L. (2007b). Bayesian compressive sensing. Accepted to IEEE Trans. on SP.
	14	MacKay, D. J. C. (1994). Bayesian methods for backpropagation networks. In E. Domany, van J. L. Hemmen and K. Schulten (Eds.), Models of neural networks III. New York: Springer-Verlag.
	15	Radford M. Neal, Bayesian Learning for Neural Networks, Springer-Verlag New York, Inc., Secaucus, NJ, 1996
	16	Scott Shaobing Chen , David L. Donoho , Michael A. Saunders, Atomic Decomposition by Basis Pursuit, SIAM Journal on Scientific Computing, v.20 n.1, p.33-61, Aug. 1998 [doi>10.1137/S1064827596304010]
	17	Sethuraman, J. (1994). A constructive de£nition of the dirichlet prior. Statistica Sinica, 2, 639--650.
	18	Michael E. Tipping, Sparse bayesian learning and the relevance vector machine, The Journal of Machine Learning Research, 1, p.211-244, 9/1/2001 [doi>10.1162/15324430152748236]
	19	Tropp, J. A., & Gilbert, A. C. (2005). Signal recovery from partial information via orthogonal matching pursuit.
	20	Yaakov Tsaig , David L. Donoho, Extensions of compressed sensing, Signal Processing, v.86 n.3, p.549-571, March 2006 [doi>10.1016/j.sigpro.2005.05.029]
	21	West, M., Muller, P., & Escobar, M. (1994). Hierarchical priors and mixture models with applications in regression and density estimation. In P. R. Freeman and A. F. Smith (Eds.), Aspects of uncertainty, 363--386. John Wiley.
	22	Wipf, D. P., & Rao, B. D. (2007). An empirical bayesian strategy for solving the simultaneous sparse approximation problem. IEEE Trans. on SP, 55, 3704--3716.