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Variational Bayesian learning of sparse representations and its application in functional neuroimaging

Abstract:

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 datase...

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Authors


Roussos, E More by this author
Roberts, S More by this author
Daubechies, I More by this author
Journal:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume:
7263 LNAI
Pages:
218-225
Publication date:
2012
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
URN:
uuid:1e8a2059-2657-41e5-bc88-b2315a511721
Source identifiers:
319077
Local pid:
pubs:319077

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