Variational Bayesian learning of sparse representations and its application in functional neuroimaging
Publication
, Journal Article
Roussos, E; Roberts, S; Daubechies, I
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
November 30, 2012
Recent theoretical and experimental work in imaging neuroscience reveals that activations inferred from functional MRI data have sparse structure. We view sparse representation as a problem in Bayesian inference, following a machine learning approach, and construct a structured generative latent-variable model employing adaptive sparsity-inducing priors. The construction allows for automatic complexity control and regularization as well as denoising. Experimental results with benchmark datasets show that the proposed algorithm outperforms standard tools for model-free decompositions such as independent component analysis. © 2012 Springer-Verlag.
Duke Scholars
Published In
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
DOI
EISSN
1611-3349
ISSN
0302-9743
Publication Date
November 30, 2012
Volume
7263 LNAI
Start / End Page
218 / 225
Related Subject Headings
- Artificial Intelligence & Image Processing
Citation
APA
Chicago
ICMJE
MLA
NLM
Roussos, E., Roberts, S., & Daubechies, I. (2012). Variational Bayesian learning of sparse representations and its application in functional neuroimaging. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7263 LNAI, 218–225. https://doi.org/10.1007/978-3-642-34713-9_28
Roussos, E., S. Roberts, and I. Daubechies. “Variational Bayesian learning of sparse representations and its application in functional neuroimaging.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7263 LNAI (November 30, 2012): 218–25. https://doi.org/10.1007/978-3-642-34713-9_28.
Roussos E, Roberts S, Daubechies I. Variational Bayesian learning of sparse representations and its application in functional neuroimaging. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2012 Nov 30;7263 LNAI:218–25.
Roussos, E., et al. “Variational Bayesian learning of sparse representations and its application in functional neuroimaging.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7263 LNAI, Nov. 2012, pp. 218–25. Scopus, doi:10.1007/978-3-642-34713-9_28.
Roussos E, Roberts S, Daubechies I. Variational Bayesian learning of sparse representations and its application in functional neuroimaging. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2012 Nov 30;7263 LNAI:218–225.
Published In
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
DOI
EISSN
1611-3349
ISSN
0302-9743
Publication Date
November 30, 2012
Volume
7263 LNAI
Start / End Page
218 / 225
Related Subject Headings
- Artificial Intelligence & Image Processing