Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke





.......................

.......................


Publications [#287202] of Ingrid Daubechies

Papers Published

  1. 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), vol. 7263 LNAI (2012), pp. 218-225, ISSN 0302-9743 [doi]
    (last updated on 2018/08/16)

    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 datasets show that the proposed algorithm outperforms standard tools for model-free decompositions such as independent component analysis. © 2012 Springer-Verlag.

 

dept@math.duke.edu
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320