Space-Alternating Generalized Expectation-Maximization Algorithm
dc.contributor.author | Fessler, Jeffrey A. | en_US |
dc.contributor.author | Hero, Alfred 0. III | en_US |
dc.date.accessioned | 2011-08-18T18:21:00Z | |
dc.date.available | 2011-08-18T18:21:00Z | |
dc.date.issued | 1994-10 | en_US |
dc.identifier.citation | Fessler, J.A.; Hero, A.O.III (1994). "Space-Alternating Generalized Expectation-Maximization Algorithm". IEEE Transactions on Signal Processing 42(10): 2664-2677. <http://hdl.handle.net/2027.42/85886> | en_US |
dc.identifier.issn | 1053-587X | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/85886 | |
dc.description.abstract | The expectation-maximization (EM) method can facilitate maximizing likelihood functions that arise in statistical estimation problems. In the classical EM paradigm, one iteratively maximizes the conditional log-likelihood of a single unobservable complete data space, rather than maximizing the intractable likelihood function for the measured or incomplete data. EM algorithms update all parameters simultaneously, which has two drawbacks: 1) slow convergence, and 2) difficult maximization steps due to coupling when smoothness penalties are used. The paper describes the space-alternating generalized EM (SAGE) method, which updates the parameters sequentially by alternating between several small hidden-data spaces defined by the algorithm designer. The authors prove that the sequence of estimates monotonically increases the penalized-likelihood objective, derive asymptotic convergence rates, and provide sufficient conditions for monotone convergence in norm. Two signal processing applications illustrate the method: estimation of superimposed signals in Gaussian noise, and image reconstruction from Poisson measurements. In both applications, the SAGE algorithms easily accommodate smoothness penalties and converge faster than the EM algorithms. | en_US |
dc.publisher | IEEE | en_US |
dc.title | Space-Alternating Generalized Expectation-Maximization Algorithm | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Biomedical Engineering | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Electrical Engineering and Computer Science. | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/85886/1/Fessler103.pdf | |
dc.identifier.doi | 10.1109/78.324732 | en_US |
dc.identifier.source | IEEE Transactions on Signal Processing | en_US |
dc.owningcollname | Electrical Engineering and Computer Science, Department of (EECS) |
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