Jason A. Palmer, Ph.D.
Affiliations: | 2006 | University of California, San Diego, La Jolla, CA |
Area:
Electronics and Electrical EngineeringGoogle:
"Jason Palmer"Parents
Sign in to add mentor| Kenneth Kreutz-Delgado | grad student | 2006 | UCSD | |
| (Variational and scale mixture representations of non -Gaussian densities for estimation in the Bayesian linear model: Sparse coding, independent component analysis, and minimum entropy segmentation.) | ||||
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Publications
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| Palmer JA, Kreutz-Delgado K, Makeig S. (2013) The linear process mixture model Ieee International Workshop On Machine Learning For Signal Processing, Mlsp |
| Delorme A, Palmer J, Onton J, et al. (2012) Independent EEG sources are dipolar. Plos One. 7: e30135 |
| Palmer JA, Kreutz-Delgado K, Makeig S. (2010) Strong sub- and super-Gaussianity Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6365: 303-310 |
| Palmer JA, Makeig S, Kreutz-Delgado K. (2009) A complex cross-spectral distribution model using normal variance mean mixtures Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 3569-3572 |
| Palmer JA, Kreutz-Delgado K, Makeig S. (2009) Probabilistic formulation of independent vector analysis using complex gaussian scale mixtures Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5441: 90-97 |
| Palmer JA, Makeig S, Kreutz-Delgado K, et al. (2008) Newton method for the ica mixture model Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 1805-1808 |
| Palmer JA, Kreutz-Delgado K, Rao BD, et al. (2007) Modeling and estimation of dependent subspaces with non-radially symmetric and skewed densities Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4666: 97-104 |
| Palmer JA, Kreutz-Delgado K, Makeig S. (2006) Super-Gaussian mixture source model for ICA Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3889: 854-861 |
| Palmer JA, Wipf DP, Kreutz-Delgado K, et al. (2005) Variational em algorithms for non-Gaussian latent variable models Advances in Neural Information Processing Systems. 1059-1066 |
| Rao BD, Engan K, Cotter SF, et al. (2003) Subset selection in noise based on diversity measure minimization Ieee Transactions On Signal Processing. 51: 760-770 |