Math @ Duke

Publications [#287172] of Ingrid Daubechies
Papers Published
 Roussos, E; Roberts, S; Daubechies, I, Variational Bayesian learning for wavelet independent component analysis,
Aip Conference Proceedings, vol. 803
(November, 2005),
pp. 274281, AIP, ISSN 0094243X [doi]
(last updated on 2019/05/24)
Abstract: In an exploratory approach to data analysis, it is often useful to consider the observations as generated from a set of latent generators or "sources" via a generally unknown mapping. For the noisy overcomplete case, where we have more sources than observations, the problem becomes extremely illposed. Solutions to such inverse problems can, in many cases, be achieved by incorporating prior knowledge about the problem, captured in the form of constraints. This setting is a natural candidate for the application of the Bayesian methodology, allowing us to incorporate "soft" constraints in a natural manner. The work described in this paper is mainly driven by problems in functional magnetic resonance imaging of the brain, for the neuroscientific goal of extracting relevant "maps" from the data. This can be stated as a 'blind' source separation problem. Recent experiments in the field of neuroscience show that these maps are sparse, in some appropriate sense. The separation problem can be solved by independent component analysis (ICA), viewed as a technique for seeking sparse components, assuming appropriate distributions for the sources. We derive a hybrid waveletICA model, transforming the signals into a domain where the modeling assumption of sparsity of the coefficients with respect to a dictionary is natural. We follow a graphical modeling formalism, viewing ICA as a probabilistic generative model. We use hierarchical source and mixing models and apply Bayesian inference to the problem. This allows us to perform model selection in order to infer the complexity of the representation, as well as automatic denoising. Since exact inference and learning in such a model is intractable, we follow a variational Bayesian meanfield approach in the conjugateexponential family of distributions, for efficient unsupervised learning in multidimensional settings. The performance of the proposed algorithm is demonstrated on some representative experiments. © 2005 American Institute of Physics.


dept@math.duke.edu
ph: 919.660.2800
fax: 919.660.2821
 
Mathematics Department
Duke University, Box 90320
Durham, NC 277080320

